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Posted to common-commits@hadoop.apache.org by su...@apache.org on 2019/02/24 09:07:17 UTC
[hadoop] branch trunk updated: YARN-8821. [YARN-8851] GPU
hierarchy/topology scheduling support based on pluggable device framework.
Contributed by Zhankun Tang.
This is an automated email from the ASF dual-hosted git repository.
sunilg pushed a commit to branch trunk
in repository https://gitbox.apache.org/repos/asf/hadoop.git
The following commit(s) were added to refs/heads/trunk by this push:
new dddcfa4 YARN-8821. [YARN-8851] GPU hierarchy/topology scheduling support based on pluggable device framework. Contributed by Zhankun Tang.
dddcfa4 is described below
commit dddcfa4d9f07aa96692a12afd3003ae89ac40b17
Author: Sunil G <su...@apache.org>
AuthorDate: Sun Feb 24 14:36:06 2019 +0530
YARN-8821. [YARN-8851] GPU hierarchy/topology scheduling support based on pluggable device framework. Contributed by Zhankun Tang.
---
.../api/deviceplugin/DevicePluginScheduler.java | 10 +-
.../com/nvidia/NvidiaGPUPluginForRuntimeV2.java | 496 +++-
.../deviceframework/DeviceMappingManager.java | 14 +-
.../nvidia/TestNvidiaGPUPluginForRuntimeV2.java | 848 ++++++
.../deviceframework/FakeTestDevicePlugin1.java | 3 +-
.../deviceframework/TestDevicePluginAdapter.java | 7 +-
.../nvidia/com/TestNvidiaGpuPlugin.java | 108 -
.../resources/tensorflow-bench-result-for-GPU.csv | 2963 ++++++++++++++++++++
8 files changed, 4327 insertions(+), 122 deletions(-)
diff --git a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/api/deviceplugin/DevicePluginScheduler.java b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/api/deviceplugin/DevicePluginScheduler.java
index 80ffdf7..479a19d 100644
--- a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/api/deviceplugin/DevicePluginScheduler.java
+++ b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/api/deviceplugin/DevicePluginScheduler.java
@@ -18,6 +18,7 @@
package org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin;
+import java.util.Map;
import java.util.Set;
/**
@@ -29,10 +30,15 @@ public interface DevicePluginScheduler {
* Called when allocating devices. The framework will do all device book
* keeping and fail recovery. So this hook could be stateless and only do
* scheduling based on available devices passed in. It could be
- * invoked multiple times by the framework.
+ * invoked multiple times by the framework. The hint in environment variables
+ * passed in could be potentially used in making better scheduling decision.
+ * For instance, GPU scheduling might support different kind of policy. The
+ * container can set it through environment variables.
* @param availableDevices Devices allowed to be chosen from.
* @param count Number of device to be allocated.
+ * @param env Environment variables of the container.
* @return A set of {@link Device} allocated
* */
- Set<Device> allocateDevices(Set<Device> availableDevices, int count);
+ Set<Device> allocateDevices(Set<Device> availableDevices, int count,
+ Map<String, String> env);
}
diff --git a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/com/nvidia/NvidiaGPUPluginForRuntimeV2.java b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/com/nvidia/NvidiaGPUPluginForRuntimeV2.java
index 609ec67..ee3f54b 100644
--- a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/com/nvidia/NvidiaGPUPluginForRuntimeV2.java
+++ b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/com/nvidia/NvidiaGPUPluginForRuntimeV2.java
@@ -24,6 +24,7 @@ import org.apache.hadoop.util.Shell;
import org.apache.hadoop.yarn.exceptions.YarnException;
import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.Device;
import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.DevicePlugin;
+import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.DevicePluginScheduler;
import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.DeviceRegisterRequest;
import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.DeviceRuntimeSpec;
import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.YarnRuntimeType;
@@ -32,7 +33,12 @@ import org.slf4j.LoggerFactory;
import java.io.File;
import java.io.IOException;
+import java.util.Arrays;
+import java.util.Collections;
import java.util.HashMap;
+import java.util.Iterator;
+import java.util.LinkedList;
+import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.TreeSet;
@@ -40,8 +46,10 @@ import java.util.TreeSet;
/**
* Nvidia GPU plugin supporting both Nvidia container runtime v2 for Docker and
* non-Docker container.
+ * It has topology aware as well as simple scheduling ability.
* */
-public class NvidiaGPUPluginForRuntimeV2 implements DevicePlugin {
+public class NvidiaGPUPluginForRuntimeV2 implements DevicePlugin,
+ DevicePluginScheduler {
public static final Logger LOG = LoggerFactory.getLogger(
NvidiaGPUPluginForRuntimeV2.class);
@@ -69,6 +77,47 @@ public class NvidiaGPUPluginForRuntimeV2 implements DevicePlugin {
private static final Set<String> DEFAULT_BINARY_SEARCH_DIRS = ImmutableSet.of(
"/usr/bin", "/bin", "/usr/local/nvidia/bin");
+ private boolean topoInitialized = false;
+
+ private Set<Device> lastTimeFoundDevices;
+
+ /**
+ * It caches the combination of different devices and the communication cost.
+ * The key is device count
+ * The value is an ordered list of map entry whose key is device combination,
+ * value is cost. The list is sorted by cost in ascending order.
+ * For instance:
+ * { 2=> [[device1,device2]=>0, [device1,device3]=>10]
+ * 3 => [[device1,device2,device3]=>10, [device2,device3,device5]=>20],
+ * }
+ * */
+ private Map<Integer, List<Map.Entry<Set<Device>, Integer>>> costTable
+ = new HashMap<>();
+
+ /**
+ * The key is a pair of minors. For instance, "0-1" indicates 0 to 1
+ * The value is weight between the two devices.
+ * */
+ private Map<String, Integer> devicePairToWeight = new HashMap<>();
+
+ /**
+ * The container can set this environment variable.
+ * To tell the scheduler what's the policy to use when do scheduling
+ * */
+ public static final String TOPOLOGY_POLICY_ENV_KEY = "NVIDIA_TOPO_POLICY";
+
+ /**
+ * Schedule policy that prefer the faster GPU-GPU communication.
+ * Suitable for heavy GPU computation workload generally.
+ * */
+ public static final String TOPOLOGY_POLICY_PACK = "PACK";
+
+ /**
+ * Schedule policy that prefer the faster CPU-GPU communication.
+ * Suitable for heavy CPU-GPU IO operations generally.
+ * */
+ public static final String TOPOLOGY_POLICY_SPREAD = "SPREAD";
+
@Override
public DeviceRegisterRequest getRegisterRequestInfo() throws Exception {
return DeviceRegisterRequest.Builder.newInstance()
@@ -106,6 +155,8 @@ public class NvidiaGPUPluginForRuntimeV2 implements DevicePlugin {
id++;
}
}
+ // cache it which help to topology scheduling
+ lastTimeFoundDevices = r;
return r;
} catch (IOException e) {
if (LOG.isDebugEnabled()) {
@@ -170,6 +221,422 @@ public class NvidiaGPUPluginForRuntimeV2 implements DevicePlugin {
return output;
}
+ @Override
+ public Set<Device> allocateDevices(Set<Device> availableDevices, int count,
+ Map<String, String> envs) {
+ Set<Device> allocation = new TreeSet<>();
+ /**
+ * corner cases.
+ * if allocate 1 device or all devices, no topo scheduling needed.
+ * if total available devices is less than 3, no topo scheduling needed.
+ * */
+ if (availableDevices.size() < 3
+ || count == 1
+ || availableDevices.size() == count) {
+ basicSchedule(allocation, count, availableDevices);
+ return allocation;
+ }
+
+ try {
+ if (!topoInitialized) {
+ initCostTable();
+ }
+ // topology aware scheduling
+ topologyAwareSchedule(allocation, count,
+ envs, availableDevices, this.costTable);
+ if (allocation.size() == count) {
+ return allocation;
+ } else {
+ LOG.error("Failed to do topology scheduling. Skip to use basic "
+ + "scheduling");
+ }
+ } catch (IOException e) {
+ LOG.error("Error in getting GPU topology info. "
+ + "Skip topology aware scheduling", e);
+ }
+ // basic scheduling
+ basicSchedule(allocation, count, availableDevices);
+ return allocation;
+ }
+
+ @VisibleForTesting
+ public void initCostTable() throws IOException {
+ // get topology
+ String topo = shellExecutor.getTopologyInfo();
+ // build the graph
+ parseTopo(topo, devicePairToWeight);
+ // build the cost table of different device combinations
+ if (lastTimeFoundDevices == null) {
+ try {
+ getDevices();
+ } catch (Exception e) {
+ LOG.error("Failed to get devices!", e);
+ return;
+ }
+ }
+ buildCostTable(costTable, lastTimeFoundDevices);
+ loggingCostTable(costTable);
+ this.topoInitialized = true;
+ }
+
+ private void loggingCostTable(
+ Map<Integer, List<Map.Entry<Set<Device>, Integer>>> cTable) {
+ if (LOG.isDebugEnabled()) {
+ StringBuilder sb = new StringBuilder("The costTable is:");
+ sb.append("\n{");
+ for (Map.Entry<Integer, List<Map.Entry<Set<Device>, Integer>>> entry
+ : cTable.entrySet()) {
+ sb.append("\n\t")
+ .append(entry.getKey())
+ .append(" => [");
+ for (Map.Entry<Set<Device>, Integer> e : entry.getValue()) {
+ sb.append("\n\t\t").append(e.toString()).append(",\n");
+ }
+ sb.append("\t\t]\n");
+ }
+ sb.append("}\n");
+ LOG.debug(sb.toString());
+ }
+ }
+
+ /**
+ * Generate combination of devices and its cost.
+ * costTable
+ * */
+ private void buildCostTable(
+ Map<Integer, List<Map.Entry<Set<Device>, Integer>>> cTable,
+ Set<Device> ltfDevices) {
+ Device[] deviceList = new Device[ltfDevices.size()];
+ ltfDevices.toArray(deviceList);
+ generateAllDeviceCombination(cTable, deviceList, deviceList.length);
+ }
+
+ /**
+ * For every possible combination of i elements.
+ * We generate a map whose key is the combination, value is cost.
+ */
+ private void generateAllDeviceCombination(
+ Map<Integer, List<Map.Entry<Set<Device>, Integer>>> cTable,
+ Device[] allDevices, int n) {
+ // allocated devices count range from 1 to n-1
+ for (int i = 2; i < n; i++) {
+ Map<Set<Device>, Integer> combinationToCost =
+ new HashMap<>();
+ buildCombination(combinationToCost, allDevices, n, i);
+ // sort the map entry by cost ascending order
+ List<Map.Entry<Set<Device>, Integer>> listSortedByCost =
+ new LinkedList<>(combinationToCost.entrySet());
+ Collections.sort(listSortedByCost,
+ (o1, o2) -> (o1.getValue()).compareTo(o2.getValue()));
+ cTable.put(i, listSortedByCost);
+ }
+ }
+
+ private void buildCombination(Map<Set<Device>, Integer> combinationToCost,
+ Device[] allDevices, int n, int r) {
+ // A temporary list to store all combination one by one
+ Device[] subDeviceList = new Device[r];
+ combinationRecursive(combinationToCost, allDevices, subDeviceList,
+ 0, n - 1, 0, r);
+ }
+
+ /**
+ * Populate combination to cost map recursively.
+ *
+ * @param cTc combinationToCost map.
+ * The key is device set, the value is cost
+ * @param allDevices all devices used to assign value to subDevicelist
+ * @param subDeviceList store a subset of devices temporary
+ * @param start start index in the allDevices
+ * @param end last index in the allDevices
+ * @param index dynamic index in subDeviceList need to be assigned
+ * @param r the length of the subDeviceList
+ */
+ void combinationRecursive(Map<Set<Device>, Integer> cTc,
+ Device[] allDevices, Device[] subDeviceList,
+ int start, int end, int index, int r) {
+ // sub device list's length is ready to compute the cost
+ if (index == r) {
+ Set<Device> oneSet = new TreeSet<>(Arrays.asList(subDeviceList));
+ int cost = computeCostOfDevices(subDeviceList);
+ cTc.put(oneSet, cost);
+ return;
+ }
+ for (int i = start; i <= end; i++) {
+ subDeviceList[index] = allDevices[i];
+ combinationRecursive(cTc, allDevices, subDeviceList,
+ i + 1, end, index + 1, r);
+ }
+ }
+
+ /**
+ * The cost function used to calculate costs of a sub set of devices.
+ * It calculate link weight of each pair in non-duplicated combination of
+ * devices.
+ */
+ @VisibleForTesting
+ public int computeCostOfDevices(Device[] devices) {
+ int cost = 0;
+ String gpuIndex0;
+ String gpuIndex1;
+ for (int i = 0; i < devices.length; i++) {
+ gpuIndex0 = String.valueOf(devices[i].getMinorNumber());
+ for (int j = i + 1; j < devices.length; j++) {
+ gpuIndex1 = String.valueOf(devices[j].getMinorNumber());
+ cost += this.devicePairToWeight.get(gpuIndex0 + "-" + gpuIndex1);
+ }
+ }
+ return cost;
+ }
+
+ /**
+ * Topology Aware schedule algorithm.
+ * It doesn't consider CPU affinity or NUMA or bus bandwidths.
+ * It support two plicy: "spread" and "pack" which can be set by container's
+ * environment variable. Use pack by default which means prefer the faster
+ * GPU-GPU. "Spread" means prefer the faster CPU-GPU.
+ * It can potentially be extend to take GPU attribute like GPU chip memory
+ * into consideration.
+ * */
+ @VisibleForTesting
+ public void topologyAwareSchedule(Set<Device> allocation, int count,
+ Map<String, String> envs,
+ Set<Device> availableDevices,
+ Map<Integer, List<Map.Entry<Set<Device>, Integer>>> cTable) {
+ int num = 0;
+ String policy = envs.get(TOPOLOGY_POLICY_ENV_KEY);
+ if (policy == null) {
+ policy = TOPOLOGY_POLICY_PACK;
+ }
+
+ /**
+ * Get combinations from costTable given the count of device want to
+ * allocate.
+ * */
+ if (cTable == null) {
+ LOG.error("No cost table initialized!");
+ return;
+ }
+ List<Map.Entry<Set<Device>, Integer>> combinationsToCost =
+ cTable.get(count);
+ Iterator<Map.Entry<Set<Device>, Integer>> iterator =
+ combinationsToCost.iterator();
+ // the container needs spread policy
+ if (policy.equalsIgnoreCase(TOPOLOGY_POLICY_SPREAD)) {
+ // loop from high cost to low cost
+ iterator = ((LinkedList) combinationsToCost).descendingIterator();
+ }
+ while (iterator.hasNext()) {
+ Map.Entry<Set<Device>, Integer> element = iterator.next();
+ if (availableDevices.containsAll(element.getKey())) {
+ allocation.addAll(element.getKey());
+ LOG.info("Topology scheduler allocated: " + allocation);
+ return;
+ }
+ }
+ LOG.error("Unknown error happened in topology scheduler");
+ }
+
+ @VisibleForTesting
+ public void basicSchedule(Set<Device> allocation, int count,
+ Set<Device> availableDevices) {
+ // Basic scheduling
+ // allocate all available
+ if (count == availableDevices.size()) {
+ allocation.addAll(availableDevices);
+ return;
+ }
+ int number = 0;
+ for (Device d : availableDevices) {
+ allocation.add(d);
+ number++;
+ if (number == count) {
+ break;
+ }
+ }
+ }
+
+ /**
+ * A typical sample topo output:
+ * GPU0 GPU1 GPU2 GPU3 CPU Affinity
+ * GPU0 X PHB SOC SOC 0-31
+ * GPU1 PHB X SOC SOC 0-31
+ * GPU2 SOC SOC X PHB 0-31
+ * GPU3 SOC SOC PHB X 0-31
+ *
+ *
+ * Legend:
+ *
+ * X = Self
+ * SOC = Connection traversing PCIe as well as the SMP link between
+ * CPU sockets(e.g. QPI)
+ * PHB = Connection traversing PCIe as well as a PCIe Host Bridge
+ * (typically the CPU)
+ * PXB = Connection traversing multiple PCIe switches
+ * (without traversing the PCIe Host Bridge)
+ * PIX = Connection traversing a single PCIe switch
+ * NV# = Connection traversing a bonded set of # NVLinks」
+ * */
+ public void parseTopo(String topo,
+ Map<String, Integer> deviceLinkToWeight) {
+ String[] lines = topo.split("\n");
+ int rowMinor;
+ int colMinor;
+ String legend;
+ String tempType;
+ for (String oneLine : lines) {
+ oneLine = oneLine.trim();
+ if (oneLine.isEmpty()) {
+ continue;
+ }
+ // To the end. No more metrics info
+ if (oneLine.startsWith("Legend")) {
+ break;
+ }
+ // Skip header
+ if (oneLine.contains("Affinity")) {
+ continue;
+ }
+ String[] tokens = oneLine.split(("\\s+"));
+ String name = tokens[0];
+ rowMinor = Integer.parseInt(name.substring(name.lastIndexOf("U") + 1));
+ for (int i = 1; i < tokens.length; i++) {
+ tempType = tokens[i];
+ colMinor = i - 1;
+ // self, skip
+ if (tempType.equals("X")) {
+ continue;
+ }
+ if (tempType.equals("SOC") || tempType.equals("SYS")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkCrossCPUSocket,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("PHB") || tempType.equals("NODE")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkSameCPUSocket,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("PXB")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkMultiSwitch,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("PIX")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkSingleSwitch,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("NV1")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkNVLink1,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("NV2")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkNVLink2,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("NV3")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkNVLink3,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("NV4")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkNVLink4,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("NV5")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkNVLink5,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("NV6")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkNVLink6,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("NV7")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkNVLink7,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("NV8")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkNVLink8,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ if (tempType.equals("NV9")) {
+ populateGraphEdgeWeight(DeviceLinkType.P2PLinkNVLink9,
+ rowMinor, colMinor, deviceLinkToWeight);
+ continue;
+ }
+ } // end one line handling
+ }
+ }
+
+ private void populateGraphEdgeWeight(
+ DeviceLinkType linkType,
+ int leftVertex,
+ int rightVertex,
+ Map<String, Integer> deviceLinkToWeight) {
+ deviceLinkToWeight.put(leftVertex + "-" + rightVertex,
+ linkType.getWeight());
+ }
+
+ /**
+ * Different type of link.
+ * The weight of each link is a relative value.
+ * The higher weight, the higher cost between the GPUs
+ * */
+ public enum DeviceLinkType {
+ /**
+ * For Nvdia GPU NVLink.
+ * */
+ P2PLinkNVLink9(10),
+ P2PLinkNVLink8(20),
+ P2PLinkNVLink7(30),
+ P2PLinkNVLink6(40),
+ P2PLinkNVLink5(50),
+ P2PLinkNVLink4(60),
+ P2PLinkNVLink3(70),
+ P2PLinkNVLink2(80),
+ P2PLinkNVLink1(90),
+
+ /**
+ * Connected to same CPU (Same NUMA node).
+ * */
+ P2PLinkSameCPUSocket(200),
+
+ /**
+ * Cross CPU through socket-level link (e.g. QPI).
+ * Usually cross NUMA node
+ * */
+ P2PLinkCrossCPUSocket(300),
+
+ /**
+ * Just need to traverse one PCIe switch to talk.
+ * */
+ P2PLinkSingleSwitch(600),
+
+ /**
+ * Need to traverse multiple PCIe switch to talk.
+ * */
+ P2PLinkMultiSwitch(1200);
+
+ // A higher link level means slower communication.
+ private int weight;
+
+ public int getWeight() {
+ return weight;
+ }
+
+ DeviceLinkType(int w) {
+ this.weight = w;
+ }
+ }
+
/**
* A shell wrapper class easy for test.
* */
@@ -189,6 +656,13 @@ public class NvidiaGPUPluginForRuntimeV2 implements DevicePlugin {
return shexec.getOutput();
}
+ // Get the topology metrics info from nvdia-smi
+ public String getTopologyInfo() throws IOException {
+ return Shell.execCommand(environment,
+ new String[]{pathOfGpuBinary, "topo",
+ "-m"}, MAX_EXEC_TIMEOUT_MS);
+ }
+
public void searchBinary() throws Exception {
if (pathOfGpuBinary != null) {
LOG.info("Skip searching, the nvidia gpu binary is already set: "
@@ -228,8 +702,8 @@ public class NvidiaGPUPluginForRuntimeV2 implements DevicePlugin {
}
@VisibleForTesting
- public void setPathOfGpuBinary(String pathOfGpuBinary) {
- this.pathOfGpuBinary = pathOfGpuBinary;
+ public void setPathOfGpuBinary(String pOfGpuBinary) {
+ this.pathOfGpuBinary = pOfGpuBinary;
}
@VisibleForTesting
@@ -237,4 +711,20 @@ public class NvidiaGPUPluginForRuntimeV2 implements DevicePlugin {
NvidiaCommandExecutor shellExecutor) {
this.shellExecutor = shellExecutor;
}
+
+ @VisibleForTesting
+ public boolean isTopoInitialized() {
+ return topoInitialized;
+ }
+
+ @VisibleForTesting
+ public Map<Integer, List<Map.Entry<Set<Device>, Integer>>> getCostTable() {
+ return costTable;
+ }
+
+ @VisibleForTesting
+ public Map<String, Integer> getDevicePairToWeight() {
+ return devicePairToWeight;
+ }
+
}
diff --git a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/DeviceMappingManager.java b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/DeviceMappingManager.java
index 9fcbf93..b620620 100644
--- a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/DeviceMappingManager.java
+++ b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/main/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/DeviceMappingManager.java
@@ -19,6 +19,7 @@
package org.apache.hadoop.yarn.server.nodemanager.containermanager.resourceplugin.deviceframework;
import com.google.common.annotations.VisibleForTesting;
+import com.google.common.collect.ImmutableMap;
import com.google.common.collect.ImmutableSet;
import com.google.common.collect.Sets;
import org.apache.commons.logging.Log;
@@ -192,7 +193,7 @@ public class DeviceMappingManager {
DevicePluginScheduler dps = devicePluginSchedulers.get(resourceName);
// Prefer DevicePluginScheduler logic
pickAndDoSchedule(allowedDevices, usedDevices, assignedDevices,
- containerId, requestedDeviceCount, resourceName, dps);
+ container, requestedDeviceCount, resourceName, dps);
// Record in state store if we allocated anything
if (!assignedDevices.isEmpty()) {
@@ -310,9 +311,11 @@ public class DeviceMappingManager {
* */
private void pickAndDoSchedule(Set<Device> allowed,
Map<Device, ContainerId> used, Set<Device> assigned,
- ContainerId containerId, int count, String resourceName,
- DevicePluginScheduler dps) throws ResourceHandlerException {
-
+ Container c, int count, String resourceName,
+ DevicePluginScheduler dps)
+ throws ResourceHandlerException {
+ ContainerId containerId = c.getContainerId();
+ Map<String, String> env = c.getLaunchContext().getEnvironment();
if (null == dps) {
if (LOG.isDebugEnabled()) {
LOG.debug("Customized device plugin scheduler is preferred "
@@ -331,7 +334,8 @@ public class DeviceMappingManager {
// Pass in unmodifiable set
Set<Device> dpsAllocated = dps.allocateDevices(
Sets.difference(allowed, used.keySet()),
- count);
+ count,
+ ImmutableMap.copyOf(env));
if (dpsAllocated.size() != count) {
throw new ResourceHandlerException(dps.getClass()
+ " should allocate " + count
diff --git a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/com/nvidia/TestNvidiaGPUPluginForRuntimeV2.java b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/com/nvidia/TestNvidiaGPUPluginForRuntimeV2.java
new file mode 100644
index 0000000..595ee08
--- /dev/null
+++ b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/com/nvidia/TestNvidiaGPUPluginForRuntimeV2.java
@@ -0,0 +1,848 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ * <p>
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * <p>
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.hadoop.yarn.server.nodemanager.containermanager.resourceplugin.com.nvidia;
+
+import org.apache.commons.lang3.ArrayUtils;
+import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.Device;
+import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.DeviceRuntimeSpec;
+import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.YarnRuntimeType;
+import org.junit.Assert;
+import org.junit.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.TreeSet;
+
+import static org.mockito.ArgumentMatchers.anyInt;
+import static org.mockito.ArgumentMatchers.anyMap;
+import static org.mockito.ArgumentMatchers.anySet;
+import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.reset;
+import static org.mockito.Mockito.spy;
+import static org.mockito.Mockito.times;
+import static org.mockito.Mockito.verify;
+import static org.mockito.Mockito.when;
+
+/**
+ * Test case for NvidiaGPUPluginForRuntimeV2 device plugin.
+ * */
+public class TestNvidiaGPUPluginForRuntimeV2 {
+
+ private static final Logger LOG =
+ LoggerFactory.getLogger(TestNvidiaGPUPluginForRuntimeV2.class);
+
+ @Test
+ public void testGetNvidiaDevices() throws Exception {
+ NvidiaGPUPluginForRuntimeV2.NvidiaCommandExecutor mockShell =
+ mock(NvidiaGPUPluginForRuntimeV2.NvidiaCommandExecutor.class);
+ String deviceInfoShellOutput =
+ "0, 00000000:04:00.0\n" +
+ "1, 00000000:82:00.0";
+ String majorMinorNumber0 = "c3:0";
+ String majorMinorNumber1 = "c3:1";
+ when(mockShell.getDeviceInfo()).thenReturn(deviceInfoShellOutput);
+ when(mockShell.getMajorMinorInfo("nvidia0"))
+ .thenReturn(majorMinorNumber0);
+ when(mockShell.getMajorMinorInfo("nvidia1"))
+ .thenReturn(majorMinorNumber1);
+ NvidiaGPUPluginForRuntimeV2 plugin = new NvidiaGPUPluginForRuntimeV2();
+ plugin.setShellExecutor(mockShell);
+ plugin.setPathOfGpuBinary("/fake/nvidia-smi");
+
+ Set<Device> expectedDevices = new TreeSet<>();
+ expectedDevices.add(Device.Builder.newInstance()
+ .setId(0).setHealthy(true)
+ .setBusID("00000000:04:00.0")
+ .setDevPath("/dev/nvidia0")
+ .setMajorNumber(195)
+ .setMinorNumber(0).build());
+ expectedDevices.add(Device.Builder.newInstance()
+ .setId(1).setHealthy(true)
+ .setBusID("00000000:82:00.0")
+ .setDevPath("/dev/nvidia1")
+ .setMajorNumber(195)
+ .setMinorNumber(1).build());
+ Set<Device> devices = plugin.getDevices();
+ Assert.assertEquals(expectedDevices, devices);
+ }
+
+ @Test
+ public void testOnDeviceAllocated() throws Exception {
+ NvidiaGPUPluginForRuntimeV2 plugin = new NvidiaGPUPluginForRuntimeV2();
+ Set<Device> allocatedDevices = new TreeSet<>();
+
+ DeviceRuntimeSpec spec = plugin.onDevicesAllocated(allocatedDevices,
+ YarnRuntimeType.RUNTIME_DEFAULT);
+ Assert.assertNull(spec);
+
+ // allocate one device
+ allocatedDevices.add(Device.Builder.newInstance()
+ .setId(0).setHealthy(true)
+ .setBusID("00000000:04:00.0")
+ .setDevPath("/dev/nvidia0")
+ .setMajorNumber(195)
+ .setMinorNumber(0).build());
+ spec = plugin.onDevicesAllocated(allocatedDevices,
+ YarnRuntimeType.RUNTIME_DOCKER);
+ Assert.assertEquals("nvidia", spec.getContainerRuntime());
+ Assert.assertEquals("0", spec.getEnvs().get("NVIDIA_VISIBLE_DEVICES"));
+
+ // two device allowed
+ allocatedDevices.add(Device.Builder.newInstance()
+ .setId(0).setHealthy(true)
+ .setBusID("00000000:82:00.0")
+ .setDevPath("/dev/nvidia1")
+ .setMajorNumber(195)
+ .setMinorNumber(1).build());
+ spec = plugin.onDevicesAllocated(allocatedDevices,
+ YarnRuntimeType.RUNTIME_DOCKER);
+ Assert.assertEquals("nvidia", spec.getContainerRuntime());
+ Assert.assertEquals("0,1", spec.getEnvs().get("NVIDIA_VISIBLE_DEVICES"));
+ }
+
+ private NvidiaGPUPluginForRuntimeV2 mockEightGPUPlugin() throws IOException {
+ String topoInfo =
+ "\tGPU0\tGPU1\tGPU2\tGPU3\tGPU4\tGPU5\tGPU6\tGPU7\tCPU Affinity\n"
+ + "GPU0\t X \tNV1\tNV1\tNV2\tNV2\tPHB\tPHB\tPHB\t0-63\n"
+ + "GPU1\tNV1\t X \tNV2\tNV1\tPHB\tNV2\tPHB\tPHB\t0-63\n"
+ + "GPU2\tNV1\tNV2\t X \tNV2\tPHB\tPHB\tNV1\tPHB\t0-63\n"
+ + "GPU3\tNV2\tNV1\tNV2\t X \tPHB\tPHB\tPHB\tNV1\t0-63\n"
+ + "GPU4\tNV2\tPHB\tPHB\tPHB\t X \tNV1\tNV1\tNV2\t0-63\n"
+ + "GPU5\tPHB\tNV2\tPHB\tPHB\tNV1\t X \tNV2\tNV1\t0-63\n"
+ + "GPU6\tPHB\tPHB\tNV1\tPHB\tNV1\tNV2\t X \tNV2\t0-63\n"
+ + "GPU7\tPHB\tPHB\tPHB\tNV1\tNV2\tNV1\tNV2\t X \t0-63\n"
+ + "\n"
+ + "Legend:\n"
+ + "\n"
+ + " X = Self\n"
+ + " SYS = Connection traversing PCIe as well as the SMP interconnect"
+ + " between NUMA nodes (e.g., QPI/UPI)\n"
+ + " NODE = Connection traversing PCIe as well as the interconnect"
+ + " between PCIe Host Bridges within a NUMA node\n"
+ + " PHB = Connection traversing PCIe as well as a PCIe Host Bridge"
+ + " (typically the CPU)\n"
+ + " PXB = Connection traversing multiple PCIe switches"
+ + " (without traversing the PCIe Host Bridge)\n"
+ + " PIX = Connection traversing a single PCIe switch\n"
+ + " NV# = Connection traversing a bonded set of # NVLinks\n";
+
+ String deviceInfoShellOutput = "0, 00000000:04:00.0\n"
+ + "1, 00000000:82:00.0\n"
+ + "2, 00000000:83:00.0\n"
+ + "3, 00000000:84:00.0\n"
+ + "4, 00000000:85:00.0\n"
+ + "5, 00000000:86:00.0\n"
+ + "6, 00000000:87:00.0\n"
+ + "7, 00000000:88:00.0";
+ String majorMinorNumber0 = "c3:0";
+ String majorMinorNumber1 = "c3:1";
+ String majorMinorNumber2 = "c3:2";
+ String majorMinorNumber3 = "c3:3";
+ String majorMinorNumber4 = "c3:4";
+ String majorMinorNumber5 = "c3:5";
+ String majorMinorNumber6 = "c3:6";
+ String majorMinorNumber7 = "c3:7";
+ NvidiaGPUPluginForRuntimeV2.NvidiaCommandExecutor mockShell =
+ mock(NvidiaGPUPluginForRuntimeV2.NvidiaCommandExecutor.class);
+ when(mockShell.getDeviceInfo()).thenReturn(deviceInfoShellOutput);
+ when(mockShell.getMajorMinorInfo("nvidia0"))
+ .thenReturn(majorMinorNumber0);
+ when(mockShell.getMajorMinorInfo("nvidia1"))
+ .thenReturn(majorMinorNumber1);
+ when(mockShell.getMajorMinorInfo("nvidia2"))
+ .thenReturn(majorMinorNumber2);
+ when(mockShell.getMajorMinorInfo("nvidia3"))
+ .thenReturn(majorMinorNumber3);
+ when(mockShell.getMajorMinorInfo("nvidia4"))
+ .thenReturn(majorMinorNumber4);
+ when(mockShell.getMajorMinorInfo("nvidia5"))
+ .thenReturn(majorMinorNumber5);
+ when(mockShell.getMajorMinorInfo("nvidia6"))
+ .thenReturn(majorMinorNumber6);
+ when(mockShell.getMajorMinorInfo("nvidia7"))
+ .thenReturn(majorMinorNumber7);
+ when(mockShell.getTopologyInfo()).thenReturn(topoInfo);
+ when(mockShell.getDeviceInfo()).thenReturn(deviceInfoShellOutput);
+
+ NvidiaGPUPluginForRuntimeV2 plugin = new NvidiaGPUPluginForRuntimeV2();
+ plugin.setShellExecutor(mockShell);
+ plugin.setPathOfGpuBinary("/fake/nvidia-smi");
+ return plugin;
+ }
+
+ private NvidiaGPUPluginForRuntimeV2 mockFourGPUPlugin() throws IOException {
+ String topoInfo = "\tGPU0\tGPU1\tGPU2\tGPU3\tCPU Affinity\n"
+ + "GPU0\t X \tPHB\tSOC\tSOC\t0-31\n"
+ + "GPU1\tPHB\t X \tSOC\tSOC\t0-31\n"
+ + "GPU2\tSOC\tSOC\t X \tPHB\t0-31\n"
+ + "GPU3\tSOC\tSOC\tPHB\t X \t0-31\n"
+ + "\n"
+ + "\n"
+ + " Legend:\n"
+ + "\n"
+ + " X = Self\n"
+ + " SOC = Connection traversing PCIe as well as the SMP link between\n"
+ + " CPU sockets(e.g. QPI)\n"
+ + " PHB = Connection traversing PCIe as well as a PCIe Host Bridge\n"
+ + " (typically the CPU)\n"
+ + " PXB = Connection traversing multiple PCIe switches\n"
+ + " (without traversing the PCIe Host Bridge)\n"
+ + " PIX = Connection traversing a single PCIe switch\n"
+ + " NV# = Connection traversing a bonded set of # NVLinks";
+
+ String deviceInfoShellOutput = "0, 00000000:04:00.0\n"
+ + "1, 00000000:82:00.0\n"
+ + "2, 00000000:83:00.0\n"
+ + "3, 00000000:84:00.0";
+ String majorMinorNumber0 = "c3:0";
+ String majorMinorNumber1 = "c3:1";
+ String majorMinorNumber2 = "c3:2";
+ String majorMinorNumber3 = "c3:3";
+ NvidiaGPUPluginForRuntimeV2.NvidiaCommandExecutor mockShell =
+ mock(NvidiaGPUPluginForRuntimeV2.NvidiaCommandExecutor.class);
+ when(mockShell.getDeviceInfo()).thenReturn(deviceInfoShellOutput);
+ when(mockShell.getMajorMinorInfo("nvidia0"))
+ .thenReturn(majorMinorNumber0);
+ when(mockShell.getMajorMinorInfo("nvidia1"))
+ .thenReturn(majorMinorNumber1);
+ when(mockShell.getMajorMinorInfo("nvidia2"))
+ .thenReturn(majorMinorNumber2);
+ when(mockShell.getMajorMinorInfo("nvidia3"))
+ .thenReturn(majorMinorNumber3);
+ when(mockShell.getTopologyInfo()).thenReturn(topoInfo);
+ when(mockShell.getDeviceInfo()).thenReturn(deviceInfoShellOutput);
+
+ NvidiaGPUPluginForRuntimeV2 plugin = new NvidiaGPUPluginForRuntimeV2();
+ plugin.setShellExecutor(mockShell);
+ plugin.setPathOfGpuBinary("/fake/nvidia-smi");
+ return plugin;
+ }
+
+ @Test
+ public void testTopologySchedulingWithPackPolicy() throws Exception {
+ NvidiaGPUPluginForRuntimeV2 plugin = mockFourGPUPlugin();
+ NvidiaGPUPluginForRuntimeV2 spyPlugin = spy(plugin);
+ // cache the total devices
+ Set<Device> allDevices = spyPlugin.getDevices();
+ // environment variable to use PACK policy
+ Map<String, String> env = new HashMap<>();
+ env.put(NvidiaGPUPluginForRuntimeV2.TOPOLOGY_POLICY_ENV_KEY,
+ NvidiaGPUPluginForRuntimeV2.TOPOLOGY_POLICY_PACK);
+ // Case 0. if available devices is less than 3, no topo scheduling needed
+ Set<Device> copyAvailableDevices = new TreeSet<>(allDevices);
+ Iterator<Device> iterator0 = copyAvailableDevices.iterator();
+ iterator0.next();
+ iterator0.remove();
+ iterator0.next();
+ iterator0.remove();
+ // Case 0. allocate 1 device
+ reset(spyPlugin);
+ Set<Device> allocation = spyPlugin.allocateDevices(copyAvailableDevices,
+ 1, env);
+ Assert.assertEquals(allocation.size(), 1);
+ verify(spyPlugin).basicSchedule(anySet(), anyInt(), anySet());
+ Assert.assertFalse(spyPlugin.isTopoInitialized());
+
+ // Case 1. allocate 1 device
+ reset(spyPlugin);
+ allocation = spyPlugin.allocateDevices(allDevices, 1, env);
+ // ensure no topology scheduling needed
+ Assert.assertEquals(allocation.size(), 1);
+ verify(spyPlugin).basicSchedule(anySet(), anyInt(), anySet());
+ reset(spyPlugin);
+ // Case 2. allocate all available
+ allocation = spyPlugin.allocateDevices(allDevices, allDevices.size(), env);
+ Assert.assertEquals(allocation.size(), allDevices.size());
+ verify(spyPlugin).basicSchedule(anySet(), anyInt(), anySet());
+ // Case 3. allocate 2 devices
+ reset(spyPlugin);
+ int count = 2;
+ Map<String, Integer> pairToWeight = spyPlugin.getDevicePairToWeight();
+ allocation = spyPlugin.allocateDevices(allDevices, count, env);
+ Assert.assertEquals(allocation.size(), count);
+ // the costTable should be init and used topology scheduling
+ verify(spyPlugin).initCostTable();
+ Assert.assertTrue(spyPlugin.isTopoInitialized());
+ verify(spyPlugin).topologyAwareSchedule(anySet(), anyInt(), anyMap(),
+ anySet(), anyMap());
+ Assert.assertEquals(allocation.size(), count);
+ Device[] allocatedDevices =
+ allocation.toArray(new Device[count]);
+ // Check weights
+ Assert.assertEquals(NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkSameCPUSocket.getWeight(),
+ spyPlugin.computeCostOfDevices(allocatedDevices));
+ // Case 4. allocate 3 devices
+ reset(spyPlugin);
+ count = 3;
+ allocation = spyPlugin.allocateDevices(allDevices, count, env);
+ Assert.assertEquals(allocation.size(), count);
+ // the costTable should be init and used topology scheduling
+ verify(spyPlugin, times(0)).initCostTable();
+ Assert.assertTrue(spyPlugin.isTopoInitialized());
+ verify(spyPlugin).topologyAwareSchedule(anySet(), anyInt(), anyMap(),
+ anySet(), anyMap());
+ Assert.assertEquals(allocation.size(), count);
+ allocatedDevices =
+ allocation.toArray(new Device[count]);
+ // check weights
+ int expectedWeight =
+ NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkSameCPUSocket.getWeight()
+ + 2 * NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkCrossCPUSocket.getWeight();
+ Assert.assertEquals(expectedWeight,
+ spyPlugin.computeCostOfDevices(allocatedDevices));
+ // Case 5. allocate 2 GPUs from three available devices
+ reset(spyPlugin);
+ Iterator<Device> iterator = allDevices.iterator();
+ iterator.next();
+ // remove GPU0
+ iterator.remove();
+ count = 2;
+ allocation = spyPlugin.allocateDevices(allDevices, count, env);
+ Assert.assertEquals(allocation.size(), count);
+ // the costTable should be init and used topology scheduling
+ verify(spyPlugin, times(0)).initCostTable();
+ Assert.assertTrue(spyPlugin.isTopoInitialized());
+ verify(spyPlugin).topologyAwareSchedule(anySet(), anyInt(), anyMap(),
+ anySet(), anyMap());
+ Assert.assertEquals(allocation.size(), count);
+ allocatedDevices =
+ allocation.toArray(new Device[count]);
+ // check weights
+ Assert.assertEquals(NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkSameCPUSocket.getWeight(),
+ spyPlugin.computeCostOfDevices(allocatedDevices));
+ // it should allocate GPU 2 and 3
+ for (Device device : allocation) {
+ if (device.getMinorNumber() == 2) {
+ Assert.assertTrue(true);
+ } else if (device.getMinorNumber() == 3) {
+ Assert.assertTrue(true);
+ } else {
+ Assert.assertTrue("Should allocate GPU 2 and 3", false);
+ }
+ }
+ }
+
+ @Test
+ public void testTopologySchedulingWithSpreadPolicy() throws Exception {
+ NvidiaGPUPluginForRuntimeV2 plugin = mockFourGPUPlugin();
+ NvidiaGPUPluginForRuntimeV2 spyPlugin = spy(plugin);
+ // cache the total devices
+ Set<Device> allDevices = spyPlugin.getDevices();
+ // environment variable to use PACK policy
+ Map<String, String> env = new HashMap<>();
+ env.put(NvidiaGPUPluginForRuntimeV2.TOPOLOGY_POLICY_ENV_KEY,
+ NvidiaGPUPluginForRuntimeV2.TOPOLOGY_POLICY_SPREAD);
+ // Case 1. allocate 1 device
+ Set<Device> allocation = spyPlugin.allocateDevices(allDevices, 1, env);
+ // ensure no topology scheduling needed
+ Assert.assertEquals(allocation.size(), 1);
+ verify(spyPlugin).basicSchedule(anySet(), anyInt(), anySet());
+ reset(spyPlugin);
+ // Case 2. allocate all available
+ allocation = spyPlugin.allocateDevices(allDevices, allDevices.size(), env);
+ Assert.assertEquals(allocation.size(), allDevices.size());
+ verify(spyPlugin).basicSchedule(anySet(), anyInt(), anySet());
+ // Case 3. allocate 2 devices
+ reset(spyPlugin);
+ int count = 2;
+ Map<String, Integer> pairToWeight = spyPlugin.getDevicePairToWeight();
+ allocation = spyPlugin.allocateDevices(allDevices, count, env);
+ Assert.assertEquals(allocation.size(), count);
+ // the costTable should be init and used topology scheduling
+ verify(spyPlugin).initCostTable();
+ Assert.assertTrue(spyPlugin.isTopoInitialized());
+ verify(spyPlugin).topologyAwareSchedule(anySet(), anyInt(), anyMap(),
+ anySet(), anyMap());
+ Assert.assertEquals(allocation.size(), count);
+ Device[] allocatedDevices =
+ allocation.toArray(new Device[count]);
+ // Check weights
+ Assert.assertEquals(NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkCrossCPUSocket.getWeight(),
+ spyPlugin.computeCostOfDevices(allocatedDevices));
+ // Case 4. allocate 3 devices
+ reset(spyPlugin);
+ count = 3;
+ allocation = spyPlugin.allocateDevices(allDevices, count, env);
+ Assert.assertEquals(allocation.size(), count);
+ // the costTable should be init and used topology scheduling
+ verify(spyPlugin, times(0)).initCostTable();
+ Assert.assertTrue(spyPlugin.isTopoInitialized());
+ verify(spyPlugin).topologyAwareSchedule(anySet(), anyInt(), anyMap(),
+ anySet(), anyMap());
+ Assert.assertEquals(allocation.size(), count);
+ allocatedDevices =
+ allocation.toArray(new Device[count]);
+ // check weights
+ int expectedWeight =
+ NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkSameCPUSocket.getWeight()
+ + 2 * NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkCrossCPUSocket.getWeight();
+ Assert.assertEquals(expectedWeight,
+ spyPlugin.computeCostOfDevices(allocatedDevices));
+ // Case 5. allocate 2 GPUs from three available devices
+ reset(spyPlugin);
+ Iterator<Device> iterator = allDevices.iterator();
+ iterator.next();
+ // remove GPU0
+ iterator.remove();
+ count = 2;
+ allocation = spyPlugin.allocateDevices(allDevices, count, env);
+ Assert.assertEquals(allocation.size(), count);
+ // the costTable should be init and used topology scheduling
+ verify(spyPlugin, times(0)).initCostTable();
+ Assert.assertTrue(spyPlugin.isTopoInitialized());
+ verify(spyPlugin).topologyAwareSchedule(anySet(), anyInt(), anyMap(),
+ anySet(), anyMap());
+ Assert.assertEquals(allocation.size(), count);
+ allocatedDevices =
+ allocation.toArray(new Device[count]);
+ // check weights
+ Assert.assertEquals(NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkCrossCPUSocket.getWeight(),
+ spyPlugin.computeCostOfDevices(allocatedDevices));
+ // it should allocate GPU 1 and 2
+ for (Device device : allocation) {
+ if (device.getMinorNumber() == 0) {
+ Assert.assertTrue("Shouldn't allocate GPU 0", false);
+ }
+ }
+ }
+
+ @Test
+ public void testCostTableWithNVlink() throws Exception {
+ NvidiaGPUPluginForRuntimeV2 plugin = mockEightGPUPlugin();
+ NvidiaGPUPluginForRuntimeV2 spyPlugin = spy(plugin);
+ // verify the device pair to weight map
+ spyPlugin.initCostTable();
+ Map<String, Integer> devicePairToWeight = spyPlugin.getDevicePairToWeight();
+ // 12 combinations when choose 2 GPUs from 8 respect the order. 8!/6!
+ Assert.assertEquals(56, devicePairToWeight.size());
+ int sameCPUWeight =
+ NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkSameCPUSocket.getWeight();
+ int nv1Weight =
+ NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkNVLink1.getWeight();
+ int nv2Weight =
+ NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkNVLink2.getWeight();
+
+ Assert.assertEquals(nv1Weight, (int)devicePairToWeight.get("0-1"));
+ Assert.assertEquals(nv1Weight, (int)devicePairToWeight.get("1-0"));
+
+ Assert.assertEquals(nv2Weight, (int)devicePairToWeight.get("0-4"));
+ Assert.assertEquals(nv2Weight, (int)devicePairToWeight.get("4-0"));
+
+ Assert.assertEquals(nv2Weight, (int)devicePairToWeight.get("0-3"));
+ Assert.assertEquals(nv2Weight, (int)devicePairToWeight.get("3-0"));
+
+ Assert.assertEquals(sameCPUWeight, (int)devicePairToWeight.get("6-3"));
+ Assert.assertEquals(sameCPUWeight, (int)devicePairToWeight.get("3-6"));
+
+ Assert.assertEquals(nv2Weight, (int)devicePairToWeight.get("6-7"));
+ Assert.assertEquals(nv2Weight, (int)devicePairToWeight.get("7-6"));
+
+ Assert.assertEquals(nv1Weight, (int)devicePairToWeight.get("1-3"));
+ Assert.assertEquals(nv1Weight, (int)devicePairToWeight.get("3-1"));
+
+ // verify cost Table
+ Map<Integer, List<Map.Entry<Set<Device>, Integer>>> costTable =
+ spyPlugin.getCostTable();
+ Assert.assertNull(costTable.get(1));
+ // C8:2 = 8!/2!/6! = 28
+ Assert.assertEquals(28, costTable.get(2).size());
+ // C8:4 = 8!/4!/4! = 70
+ Assert.assertEquals(70, costTable.get(4).size());
+ Assert.assertNull(costTable.get(8));
+
+ Set<Device> allDevices = spyPlugin.getDevices();
+ Map<String, String> env = new HashMap<>();
+ env.put(NvidiaGPUPluginForRuntimeV2.TOPOLOGY_POLICY_ENV_KEY,
+ NvidiaGPUPluginForRuntimeV2.TOPOLOGY_POLICY_PACK);
+ spyPlugin.allocateDevices(allDevices, 3, env);
+ spyPlugin.allocateDevices(allDevices, 2, env);
+ }
+
+ /**
+ * Test the key cost table used for topology scheduling.
+ * */
+ @Test
+ public void testCostTable() throws IOException {
+ NvidiaGPUPluginForRuntimeV2 plugin = mockFourGPUPlugin();
+ NvidiaGPUPluginForRuntimeV2 spyPlugin = spy(plugin);
+ // verify the device pair to weight map
+ spyPlugin.initCostTable();
+ Map<String, Integer> devicePairToWeight = spyPlugin.getDevicePairToWeight();
+ // 12 combinations when choose 2 GPUs from 4 respect the order. 4!/2!
+ Assert.assertEquals(12, devicePairToWeight.size());
+ int sameCPUWeight =
+ NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkSameCPUSocket.getWeight();
+ int crossCPUWeight =
+ NvidiaGPUPluginForRuntimeV2.DeviceLinkType
+ .P2PLinkCrossCPUSocket.getWeight();
+ Assert.assertEquals(sameCPUWeight, (int)devicePairToWeight.get("0-1"));
+ Assert.assertEquals(sameCPUWeight, (int)devicePairToWeight.get("1-0"));
+
+ Assert.assertEquals(crossCPUWeight, (int)devicePairToWeight.get("0-2"));
+ Assert.assertEquals(crossCPUWeight, (int)devicePairToWeight.get("2-0"));
+
+ Assert.assertEquals(crossCPUWeight, (int)devicePairToWeight.get("0-3"));
+ Assert.assertEquals(crossCPUWeight, (int)devicePairToWeight.get("3-0"));
+
+ Assert.assertEquals(crossCPUWeight, (int)devicePairToWeight.get("1-2"));
+ Assert.assertEquals(crossCPUWeight, (int)devicePairToWeight.get("2-1"));
+
+ Assert.assertEquals(crossCPUWeight, (int)devicePairToWeight.get("1-3"));
+ Assert.assertEquals(crossCPUWeight, (int)devicePairToWeight.get("3-1"));
+
+ Assert.assertEquals(sameCPUWeight, (int)devicePairToWeight.get("2-3"));
+ Assert.assertEquals(sameCPUWeight, (int)devicePairToWeight.get("3-2"));
+
+ // verify cost Table
+ Map<Integer, List<Map.Entry<Set<Device>, Integer>>> costTable =
+ spyPlugin.getCostTable();
+ Assert.assertNull(costTable.get(1));
+ Assert.assertEquals(6, costTable.get(2).size());
+ Assert.assertEquals(4, costTable.get(3).size());
+ Assert.assertNull(costTable.get(4));
+ }
+ /**
+ * Test GPU topology allocation.
+ * And analysis the GPU allocation's performance against the actual
+ * performance data using tensorflow benchmarks.
+ * https://github.com/tensorflow/benchmarks
+ * */
+ @Test
+ public void testTopologySchedulingPerformanceWithPackPolicyWithNVLink()
+ throws Exception {
+ NvidiaGPUPluginForRuntimeV2 plugin = mockEightGPUPlugin();
+ NvidiaGPUPluginForRuntimeV2 spyPlugin = spy(plugin);
+ Set<Device> allDevices = spyPlugin.getDevices();
+ Map<String, String> env = new HashMap<>();
+ env.put(NvidiaGPUPluginForRuntimeV2.TOPOLOGY_POLICY_ENV_KEY,
+ NvidiaGPUPluginForRuntimeV2.TOPOLOGY_POLICY_PACK);
+
+ /**
+ * Analyze performance against the real data.
+ * Get the topology scheduling algorithm's allocation's
+ * average performance boost against median imagePerSecond and minimum
+ * imagePerSecond in certain model and batch size combinations.
+ * And then calculate the average performance boost.
+ * The average performance boost against
+ * median value means topology scheduler's allocation can stably
+ * outperforms 50% of possible allocations.
+ * The average performance boost against min value means the average boost
+ * comparing to the worst allocations in various scenarios. Which is more
+ * beautiful number for public promotion.
+ * And also the analysis shows the best performance boost against median
+ * and min value.
+ * */
+ ActualPerformanceReport report = new ActualPerformanceReport();
+ report.readFromFile();
+ ArrayList<ActualPerformanceReport.DataRecord> dataSet =
+ report.getDataSet();
+ Assert.assertEquals(dataSet.size(), 2952);
+ String[] allModels = {"alexnet", "resnet50", "vgg16", "inception3"};
+ int[] batchSizes = {32, 64, 128};
+ int[] gpuCounts = {2, 3, 4, 5, 6, 7};
+ float totalBoostAgainstMedian = 0;
+ int count = 0;
+ float maxBoostAgainstMedian = 0;
+ float totalBoostAgainstMin = 0;
+ float maxBoostAgainstMin = 0;
+ for (String model : allModels) {
+ float totalBoostAgainstMinCertainModel = 0;
+ float totalBoostAgainstMedianCertainModel = 0;
+ float maxBoostAgainstMinCertainModel = 0;
+ float maxBoostAgainstMedianCertainModel = 0;
+ int countOfEachModel = 0;
+ for (int bs : batchSizes) {
+ for (int gpuCount: gpuCounts) {
+ float bstAgainstMedian = calculatePerformanceBoostAgainstMedian(
+ report, model, bs, gpuCount, plugin, allDevices, env);
+ float bstAgainstMinimum = calculatePerformanceBoostAgainstMinimum(
+ report, model, bs, gpuCount, plugin, allDevices, env);
+ totalBoostAgainstMedian += bstAgainstMedian;
+ totalBoostAgainstMin += bstAgainstMinimum;
+ count++;
+ if (maxBoostAgainstMedian < bstAgainstMedian) {
+ maxBoostAgainstMedian = bstAgainstMedian;
+ }
+ if (maxBoostAgainstMin < bstAgainstMinimum) {
+ maxBoostAgainstMin = bstAgainstMinimum;
+ }
+ totalBoostAgainstMinCertainModel += bstAgainstMinimum;
+ totalBoostAgainstMedianCertainModel += bstAgainstMedian;
+ if (maxBoostAgainstMinCertainModel < bstAgainstMinimum) {
+ maxBoostAgainstMinCertainModel = bstAgainstMinimum;
+ }
+ if (maxBoostAgainstMedianCertainModel < bstAgainstMedian) {
+ maxBoostAgainstMedianCertainModel = bstAgainstMedian;
+ }
+ countOfEachModel++;
+ }
+ }
+ LOG.info("Model:{}, The best performance boost against median value is "
+ + "{}", model, maxBoostAgainstMedianCertainModel);
+ LOG.info("Model:{}, The aggregated average performance boost against "
+ + "median value is {}",
+ model, totalBoostAgainstMedianCertainModel/countOfEachModel);
+ LOG.info("Model:{}, The best performance boost against min value is {}",
+ model, maxBoostAgainstMinCertainModel);
+ LOG.info("Model:{}, The aggregated average performance boost against "
+ + "min value is {}",
+ model, totalBoostAgainstMinCertainModel/countOfEachModel);
+ }
+ LOG.info("For all, the best performance boost against median value is "
+ + maxBoostAgainstMedian);
+ LOG.info("For all, the aggregated average performance boost against median "
+ + "value is " + totalBoostAgainstMedian/count);
+ LOG.info("For all, the best performance boost against min value is "
+ + maxBoostAgainstMin);
+ LOG.info("For all, the aggregated average performance boost against min "
+ + "value is " + totalBoostAgainstMin/count);
+ }
+
+ /**
+ * For <code>gpuCount</code> GPUs allocated by the topology algorithm, return
+ * its performance boost against the median value.
+ *
+ * */
+ private float calculatePerformanceBoostAgainstMedian(
+ ActualPerformanceReport report,
+ String model, int bs, int gpuCount,
+ NvidiaGPUPluginForRuntimeV2 plugin, Set<Device> allDevice,
+ Map<String, String> env) {
+ Set<Device> allocation = plugin.allocateDevices(allDevice, gpuCount, env);
+ String gpuAllocationString = convertAllocationToGpuString(allocation);
+ float[] metrics = report.getVariousImagePerSecond(model, bs,
+ gpuCount, gpuAllocationString);
+ return metrics[7];
+ }
+
+ /**
+ * For <code>gpuCount</code> GPUs allocated by the topology algorithm, return
+ * its performance boost against the minimum value.
+ *
+ * */
+ private float calculatePerformanceBoostAgainstMinimum(
+ ActualPerformanceReport report,
+ String model, int bs, int gpuCount,
+ NvidiaGPUPluginForRuntimeV2 plugin, Set<Device> allDevice,
+ Map<String, String> env) {
+ Set<Device> allocation = plugin.allocateDevices(allDevice, gpuCount, env);
+ String gpuAllocationString = convertAllocationToGpuString(allocation);
+ float[] metrics = report.getVariousImagePerSecond(model, bs,
+ gpuCount, gpuAllocationString);
+ return metrics[5];
+ }
+
+ private String convertAllocationToGpuString(Set<Device> allocation) {
+ StringBuilder sb = new StringBuilder();
+ for (Device device : allocation) {
+ sb.append(device.getMinorNumber() + "_");
+ }
+ return sb.toString().substring(0, sb.lastIndexOf("_"));
+ }
+
+ /**
+ * Representation of the performance data report.
+ * */
+ private class ActualPerformanceReport {
+
+ private ArrayList<DataRecord> dataSet = new ArrayList<>();
+
+ public ArrayList<DataRecord> getDataSet() {
+ return dataSet;
+ }
+
+ /**
+ * One line in the report.
+ * */
+ private class DataRecord {
+ DataRecord(String model, int bs, String combination, float fps,
+ int count) {
+ this.batchSize = bs;
+ this.gpuCombination = combination;
+ this.gpuCount = count;
+ this.model = model;
+ this.imagePerSecond = fps;
+ }
+
+ public String getModel() {
+ return model;
+ }
+
+ public int getBatchSize() {
+ return batchSize;
+ }
+
+ public String getGpuCombination() {
+ return gpuCombination;
+ }
+
+ public float getImagePerSecond() {
+ return imagePerSecond;
+ }
+
+ public int getGpuCount() {
+ return gpuCount;
+ }
+
+ private String model;
+ private int batchSize;
+ private String gpuCombination;
+ private float imagePerSecond;
+ private int gpuCount;
+ }
+
+ /**
+ * The file is a real performance report got from a 8 GPUs AWS instance.
+ * It contains every combination GPUs' training performance of Tensorflow
+ * benchmark.
+ * The columns are the model name, batch size, gpu ids and imagesPerSecond
+ * */
+ public void readFromFile() {
+ String csvReportFilePath = getClass().getClassLoader()
+ .getResource("tensorflow-bench-result-for-GPU.csv").getFile();
+ BufferedReader br = null;
+ String line = "";
+ try {
+ br = new BufferedReader(new FileReader(csvReportFilePath));
+ String model;
+ int batchSize;
+ String gpuCombination;
+ float imagePerSecond;
+ int gpuCount;
+ while ((line = br.readLine()) != null) {
+ // skip the licence content
+ if (line.startsWith("#")) {
+ continue;
+ }
+ String[] tokens = line.replaceAll("\"", "").split(",");
+ if (tokens.length != 4) {
+ LOG.error("unexpected performance data format!");
+ break;
+ }
+ model = tokens[0];
+ batchSize = Integer.parseInt(tokens[1].trim());
+ gpuCombination = tokens[2];
+ imagePerSecond = Float.parseFloat(tokens[3]);
+ gpuCount = getGpuCount(gpuCombination);
+ this.dataSet.add(new DataRecord(model, batchSize, gpuCombination,
+ imagePerSecond, gpuCount));
+ }
+ } catch (Exception e) {
+ e.printStackTrace();
+ } finally {
+ if (br != null) {
+ try {
+ br.close();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+ }
+ } // end finally
+ }
+
+ /**
+ * Return the maximum, minimum, mean and median performance for model &
+ * bs & gpuCount. And the imagePerSecond for model & bs & gpuCount &
+ * gpuCombinations. And imagePerSecond performance boost comparing to
+ * minimum, mean and media value.
+ * */
+ private float[] getVariousImagePerSecond(String model, int bs,
+ int gpuCount, String gpuCombinations) {
+ float[] result = new float[8];
+ float max = 0;
+ float min = Float.MAX_VALUE;
+ float sum = 0;
+ int count = 0;
+ float wantedImagePerSecond = 0;
+ float currentImagePerSecond;
+ ArrayList<Float> allFps = new ArrayList<>();
+ for (DataRecord dr : getDataSet()) {
+ currentImagePerSecond = dr.getImagePerSecond();
+ if (dr.batchSize == bs
+ && model.equals(dr.getModel())
+ && gpuCount == dr.getGpuCount()) {
+ sum += currentImagePerSecond;
+ count++;
+ if (max < currentImagePerSecond) {
+ max = currentImagePerSecond;
+ }
+ if (min > currentImagePerSecond) {
+ min = currentImagePerSecond;
+ }
+ if (gpuCombinations.equals(dr.getGpuCombination())) {
+ wantedImagePerSecond = dr.getImagePerSecond();
+ }
+ allFps.add(dr.getImagePerSecond());
+ }
+ }
+ float median = getMedian(allFps);
+ float mean = sum/count;
+ result[0] = max;
+ result[1] = min;
+ result[2] = mean;
+ result[3] = median;
+ result[4] = wantedImagePerSecond;
+ result[5] = wantedImagePerSecond/min - 1;
+ result[6] = wantedImagePerSecond/mean - 1;
+ result[7] = wantedImagePerSecond/median - 1;
+ return result;
+ }
+
+ private float getMedian(ArrayList<Float> allFps) {
+ float[] all = ArrayUtils.toPrimitive(allFps.toArray(new Float[0]), 0);
+ Arrays.sort(all);
+ float median;
+ int size = all.length;
+ if (allFps.size() % 2 == 0) {
+ median = (all[size/2] + all[size/2 - 1])/2;
+ } else {
+ median = all[size/2];
+ }
+ return median;
+ }
+
+ private int getGpuCount(String gpuCombination) {
+ String[] tokens = gpuCombination.split("_");
+ return tokens.length;
+ }
+ }
+
+}
diff --git a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/FakeTestDevicePlugin1.java b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/FakeTestDevicePlugin1.java
index 12f1064..69736fd 100644
--- a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/FakeTestDevicePlugin1.java
+++ b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/FakeTestDevicePlugin1.java
@@ -20,6 +20,7 @@ package org.apache.hadoop.yarn.server.nodemanager.containermanager.resourceplugi
import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.*;
+import java.util.Map;
import java.util.Set;
import java.util.TreeSet;
@@ -62,7 +63,7 @@ public class FakeTestDevicePlugin1
@Override
public Set<Device> allocateDevices(Set<Device> availableDevices,
- int count) {
+ int count, Map<String, String> env) {
Set<Device> allocated = new TreeSet<Device>();
int number = 0;
for (Device d : availableDevices) {
diff --git a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/TestDevicePluginAdapter.java b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/TestDevicePluginAdapter.java
index 75668f2..2a64522 100644
--- a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/TestDevicePluginAdapter.java
+++ b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/deviceframework/TestDevicePluginAdapter.java
@@ -74,10 +74,11 @@ import java.util.Set;
import java.util.TreeSet;
import java.util.concurrent.ConcurrentHashMap;
-
import static org.mockito.ArgumentMatchers.any;
import static org.mockito.ArgumentMatchers.anyBoolean;
import static org.mockito.ArgumentMatchers.anyInt;
+import static org.mockito.ArgumentMatchers.anyMap;
+import static org.mockito.ArgumentMatchers.anySet;
import static org.mockito.ArgumentMatchers.anyString;
import static org.mockito.ArgumentMatchers.eq;
import static org.mockito.ArgumentMatchers.isA;
@@ -571,7 +572,7 @@ public class TestDevicePluginAdapter {
adapter.getDeviceResourceHandler().preStart(c1);
// Use customized scheduler
verify(spyPlugin, times(1)).allocateDevices(
- any(Set.class), anyInt());
+ anySet(), anyInt(), anyMap());
Assert.assertEquals(2,
dmm.getAvailableDevices(resourceName));
Assert.assertEquals(1,
@@ -995,7 +996,7 @@ public class TestDevicePluginAdapter {
@Override
public Set<Device> allocateDevices(Set<Device> availableDevices,
- int count) {
+ int count, Map<String, String> env) {
Set<Device> allocated = new TreeSet<>();
int number = 0;
for (Device d : availableDevices) {
diff --git a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/nvidia/com/TestNvidiaGpuPlugin.java b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/nvidia/com/TestNvidiaGpuPlugin.java
deleted file mode 100644
index 33154d8..0000000
--- a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/java/org/apache/hadoop/yarn/server/nodemanager/containermanager/resourceplugin/nvidia/com/TestNvidiaGpuPlugin.java
+++ /dev/null
@@ -1,108 +0,0 @@
-/**
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements. See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership. The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License. You may obtain a copy of the License at
- * <p>
- * http://www.apache.org/licenses/LICENSE-2.0
- * <p>
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.hadoop.yarn.server.nodemanager.containermanager.resourceplugin.nvidia.com;
-
-import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.Device;
-import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.DeviceRuntimeSpec;
-import org.apache.hadoop.yarn.server.nodemanager.api.deviceplugin.YarnRuntimeType;
-import org.apache.hadoop.yarn.server.nodemanager.containermanager.resourceplugin.com.nvidia.NvidiaGPUPluginForRuntimeV2;
-import org.junit.Assert;
-import org.junit.Test;
-
-import java.util.Set;
-import java.util.TreeSet;
-
-import static org.mockito.Mockito.mock;
-import static org.mockito.Mockito.when;
-
-/**
- * Test case for Nvidia GPU device plugin.
- * */
-public class TestNvidiaGpuPlugin {
-
- @Test
- public void testGetNvidiaDevices() throws Exception {
- NvidiaGPUPluginForRuntimeV2.NvidiaCommandExecutor mockShell =
- mock(NvidiaGPUPluginForRuntimeV2.NvidiaCommandExecutor.class);
- String deviceInfoShellOutput =
- "0, 00000000:04:00.0\n" +
- "1, 00000000:82:00.0";
- String majorMinorNumber0 = "c3:0";
- String majorMinorNumber1 = "c3:1";
- when(mockShell.getDeviceInfo()).thenReturn(deviceInfoShellOutput);
- when(mockShell.getMajorMinorInfo("nvidia0"))
- .thenReturn(majorMinorNumber0);
- when(mockShell.getMajorMinorInfo("nvidia1"))
- .thenReturn(majorMinorNumber1);
- NvidiaGPUPluginForRuntimeV2 plugin = new NvidiaGPUPluginForRuntimeV2();
- plugin.setShellExecutor(mockShell);
- plugin.setPathOfGpuBinary("/fake/nvidia-smi");
-
- Set<Device> expectedDevices = new TreeSet<>();
- expectedDevices.add(Device.Builder.newInstance()
- .setId(0).setHealthy(true)
- .setBusID("00000000:04:00.0")
- .setDevPath("/dev/nvidia0")
- .setMajorNumber(195)
- .setMinorNumber(0).build());
- expectedDevices.add(Device.Builder.newInstance()
- .setId(1).setHealthy(true)
- .setBusID("00000000:82:00.0")
- .setDevPath("/dev/nvidia1")
- .setMajorNumber(195)
- .setMinorNumber(1).build());
- Set<Device> devices = plugin.getDevices();
- Assert.assertEquals(expectedDevices, devices);
- }
-
- @Test
- public void testOnDeviceAllocated() throws Exception {
- NvidiaGPUPluginForRuntimeV2 plugin = new NvidiaGPUPluginForRuntimeV2();
- Set<Device> allocatedDevices = new TreeSet<>();
-
- DeviceRuntimeSpec spec = plugin.onDevicesAllocated(allocatedDevices,
- YarnRuntimeType.RUNTIME_DEFAULT);
- Assert.assertNull(spec);
-
- // allocate one device
- allocatedDevices.add(Device.Builder.newInstance()
- .setId(0).setHealthy(true)
- .setBusID("00000000:04:00.0")
- .setDevPath("/dev/nvidia0")
- .setMajorNumber(195)
- .setMinorNumber(0).build());
- spec = plugin.onDevicesAllocated(allocatedDevices,
- YarnRuntimeType.RUNTIME_DOCKER);
- Assert.assertEquals("nvidia", spec.getContainerRuntime());
- Assert.assertEquals("0", spec.getEnvs().get("NVIDIA_VISIBLE_DEVICES"));
-
- // two device allowed
- allocatedDevices.add(Device.Builder.newInstance()
- .setId(0).setHealthy(true)
- .setBusID("00000000:82:00.0")
- .setDevPath("/dev/nvidia1")
- .setMajorNumber(195)
- .setMinorNumber(1).build());
- spec = plugin.onDevicesAllocated(allocatedDevices,
- YarnRuntimeType.RUNTIME_DOCKER);
- Assert.assertEquals("nvidia", spec.getContainerRuntime());
- Assert.assertEquals("0,1", spec.getEnvs().get("NVIDIA_VISIBLE_DEVICES"));
-
- }
-}
diff --git a/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/resources/tensorflow-bench-result-for-GPU.csv b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/resources/tensorflow-bench-result-for-GPU.csv
new file mode 100644
index 0000000..0ff6f3d
--- /dev/null
+++ b/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-nodemanager/src/test/resources/tensorflow-bench-result-for-GPU.csv
@@ -0,0 +1,2963 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"alexnet","32","0_2","2910.87"
+"resnet50","32","0_2","601.82"
+"vgg16","32","0_2","382.83"
+"inception3","32","0_2","392.53"
+"alexnet","64","0_2","4539.27"
+"resnet50","64","0_2","699.90"
+"vgg16","64","0_2","428.95"
+"inception3","64","0_2","435.53"
+"alexnet","128","0_2","6319.08"
+"resnet50","128","0_2","781.39"
+"vgg16","128","0_2","454.85"
+"inception3","128","0_2","456.94"
+"alexnet","32","6_7","3922.30"
+"resnet50","32","6_7","588.72"
+"vgg16","32","6_7","406.52"
+"inception3","32","6_7","380.24"
+"alexnet","64","6_7","5693.85"
+"resnet50","64","6_7","694.92"
+"vgg16","64","6_7","437.44"
+"inception3","64","6_7","429.96"
+"alexnet","128","6_7","7226.03"
+"resnet50","128","6_7","764.63"
+"vgg16","128","6_7","451.29"
+"inception3","128","6_7","454.66"
+"alexnet","32","1_6","1552.58"
+"resnet50","32","1_6","558.30"
+"vgg16","32","1_6","315.01"
+"inception3","32","1_6","375.01"
+"alexnet","64","1_6","2829.13"
+"resnet50","64","1_6","680.00"
+"vgg16","64","1_6","382.46"
+"inception3","64","1_6","427.11"
+"alexnet","128","1_6","4448.61"
+"resnet50","128","1_6","767.72"
+"vgg16","128","1_6","425.83"
+"inception3","128","1_6","453.58"
+"alexnet","32","0_7","1559.17"
+"resnet50","32","0_7","562.46"
+"vgg16","32","0_7","313.64"
+"inception3","32","0_7","379.64"
+"alexnet","64","0_7","2861.61"
+"resnet50","64","0_7","673.91"
+"vgg16","64","0_7","381.18"
+"inception3","64","0_7","424.98"
+"alexnet","128","0_7","4453.04"
+"resnet50","128","0_7","754.64"
+"vgg16","128","0_7","419.01"
+"inception3","128","0_7","447.85"
+"alexnet","32","2_7","1565.90"
+"resnet50","32","2_7","563.24"
+"vgg16","32","2_7","310.85"
+"inception3","32","2_7","377.70"
+"alexnet","64","2_7","2837.95"
+"resnet50","64","2_7","672.73"
+"vgg16","64","2_7","377.73"
+"inception3","64","2_7","428.27"
+"alexnet","128","2_7","4462.88"
+"resnet50","128","2_7","759.73"
+"vgg16","128","2_7","418.70"
+"inception3","128","2_7","453.87"
+"alexnet","32","3_4","1564.28"
+"resnet50","32","3_4","562.02"
+"vgg16","32","3_4","314.93"
+"inception3","32","3_4","379.44"
+"alexnet","64","3_4","2852.29"
+"resnet50","64","3_4","675.38"
+"vgg16","64","3_4","382.05"
+"inception3","64","3_4","429.81"
+"alexnet","128","3_4","4496.05"
+"resnet50","128","3_4","759.31"
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