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svn commit: r1782721 [20/24] - in /incubator/singa/site/trunk/v1.1.0: ./
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Added: incubator/singa/site/trunk/v1.1.0/docs/software_stack.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/software_stack.html?rev=1782721&view=auto
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+ <div class="section" id="software-stack">
+<span id="software-stack"></span><h1>Software Stack<a class="headerlink" href="#software-stack" title="Permalink to this headline">¶</a></h1>
+<p>SINGA’s software stack includes three major components, namely, core, IO and
+model. Figure 1 illustrates these components together with the hardware.
+The core component provides memory management and tensor operations;
+IO has classes for reading (and writing) data from (to) disk and network; The
+model component provides data structures and algorithms for machine learning models,
+e.g., layers for neural network models, optimizers/initializer/metric/loss for
+general machine learning models.</p>
+<p><img src="../_static/images/singav1-sw.png" align="center" width="500px"/>
+<br/>
+<span><strong>Figure 1 - SINGA V1 software stack.</strong></span></p>
+<div class="section" id="core">
+<span id="core"></span><h2>Core<a class="headerlink" href="#core" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference external" href="tensor.html">Tensor</a> and <a class="reference external" href="device.html">Device</a> are two core abstractions in SINGA. Tensor class represents a
+multi-dimensional array, which stores model variables and provides linear algebra
+operations for machine learning
+algorithms, including matrix multiplication and random functions. Each tensor
+instance (i.e. a tensor) is allocated on a Device instance.
+Each Device instance (i.e. a device) is created against one hardware device,
+e.g. a GPU card or a CPU core. Devices manage the memory of tensors and execute
+tensor operations on its execution units, e.g. CPU threads or CUDA streams.</p>
+<p>Depending on the hardware and the programming language, SINGA have implemented
+the following specific device classes:</p>
+<ul class="simple">
+<li><strong>CudaGPU</strong> represents an Nvidia GPU card. The execution units are the CUDA streams.</li>
+<li><strong>CppCPU</strong> represents a normal CPU. The execution units are the CPU threads.</li>
+<li><strong>OpenclGPU</strong> represents normal GPU card from both Nvidia and AMD.
+The execution units are the CommandQueues. Given that OpenCL is compatible with
+many hardware devices, e.g. FPGA and ARM, the OpenclGPU has the potential to be
+extended for other devices.</li>
+</ul>
+<p>Different types of devices use different programming languages to write the kernel
+functions for tensor operations,</p>
+<ul class="simple">
+<li>CppMath (tensor_math_cpp.h) implements the tensor operations using Cpp for CppCPU</li>
+<li>CudaMath (tensor_math_cuda.h) implements the tensor operations using CUDA for CudaGPU</li>
+<li>OpenclMath (tensor_math_opencl.h) implements the tensor operations using OpenCL for OpenclGPU</li>
+</ul>
+<p>In addition, different types of data, such as float32 and float16, could be supported by adding
+the corresponding tensor functions.</p>
+<p>Typically, users would create a device instance and pass it to create multiple
+tensor instances. When users call the Tensor functions, these function would invoke
+the corresponding implementation (CppMath/CudaMath/OpenclMath) automatically. In
+other words, the implementation of Tensor operations is transparent to users.</p>
+<p>Most machine learning algorithms could be expressed using (dense or sparse) tensors.
+Therefore, with the Tensor abstraction, SINGA would be able to run a wide range of models,
+including deep learning models and other traditional machine learning models.</p>
+<p>The Tensor and Device abstractions are extensible to support a wide range of hardware device
+using different programming languages. A new hardware device would be supported by
+adding a new Device subclass and the corresponding implementation of the Tensor
+operations (xxxMath).</p>
+<p>Optimizations in terms of speed and memory could be implemented by Device, which
+manages both operation execution and memory malloc/free. More optimization details
+would be described in the <a class="reference external" href="device.html">Device page</a>.</p>
+</div>
+<div class="section" id="model">
+<span id="model"></span><h2>Model<a class="headerlink" href="#model" title="Permalink to this headline">¶</a></h2>
+<p>On top of the Tensor and Device abstractions, SINGA provides some higher level
+classes for machine learning modules.</p>
+<ul class="simple">
+<li><a class="reference external" href="layer.html">Layer</a> and its subclasses are specific for neural networks. Every layer provides
+functions for forward propagating features and backward propagating gradients w.r.t the training loss functions.
+They wraps the complex layer operations so that users can easily create neural nets
+by connecting a set of layers.</li>
+<li><a class="reference external" href="initializer.html">Initializer</a> and its subclasses provide variant methods of initializing
+model parameters (stored in Tensor instances), following Uniform, Gaussian, etc.</li>
+<li><a class="reference external" href="loss.html">Loss</a> and its subclasses defines the training objective loss functions.
+Both functions of computing the loss values and computing the gradient of the prediction w.r.t the
+objective loss are implemented. Example loss functions include squared error and cross entropy.</li>
+<li><a class="reference external" href="metric.html">Metric</a> and its subclasses provide the function to measure the
+performance of the model, e.g., the accuracy.</li>
+<li><a class="reference external" href="optimizer.html">Optimizer</a> and its subclasses implement the methods for updating
+model parameter values using parameter gradients, including SGD, AdaGrad, RMSProp etc.</li>
+</ul>
+</div>
+<div class="section" id="io">
+<span id="io"></span><h2>IO<a class="headerlink" href="#io" title="Permalink to this headline">¶</a></h2>
+<p>The IO module consists of classes for data loading, data preprocessing and message passing.</p>
+<ul class="simple">
+<li>Reader and its subclasses load string records from disk files</li>
+<li>Writer and its subclasses write string records to disk files</li>
+<li>Encoder and its subclasses encode Tensor instances into string records</li>
+<li>Decoder and its subclasses decodes string records into Tensor instances</li>
+<li>Endpoint represents a communication endpoint which provides functions for passing messages to each other.</li>
+<li>Message represents communication messages between Endpoint instances. It carries both meta data and payload.</li>
+</ul>
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