[GitHub] haojin2 edited a comment on issue #11330: [MXNET-537] add_n(dense, csr, dense) = dense and add_n([dense, csr, rsp]*, dense, [dense, csr, rsp]*) = dense on CPU & GPU

[GitBox <gi...@apache.org>]

haojin2 edited a comment on issue #11330: [MXNET-537] add_n(dense, csr, dense) = dense and add_n([dense, csr, rsp]*, dense, [dense, csr, rsp]*) = dense on CPU & GPU
URL: https://github.com/apache/incubator-mxnet/pull/11330#issuecomment-398583696
 
 
   Benchmark result for add_n(more than 4 inputs with at least 1 dense) = dense (the combination being benchmarked here is add_n(dense, csr, dense, rsp, dense) = dense):
   ([density%] [speedup])
   CPU:
   1.00% 1.4248320861874664
   0.50% 1.4591373125830511
   0.10% 1.487516900293522
   0.05% 1.4891773584928327
   0.01% 1.4833875047500007
   GPU:
   1.00% 1.5829503717448206
   0.50% 1.612348854910054
   0.10% 1.6657770987040201
   0.05% 1.6743607944367647
   0.01% 1.6844786052948375
   Benchmark script:
   ```python
   import mxnet as mx
   import sys
   import os
   import scipy
   import numpy as np
   from mxnet.test_utils import rand_ndarray, assert_almost_equal
   import time
   
   def measure_cost(repeat, a, b, c, d, e, out=None):
       # start bench
       start = time.time()
       results = []
       for i in range(repeat):
           results.append(mx.nd.sparse.add_n(a, b, c, d, e, out=out))
       for result in results:
           result.wait_to_read()
       end = time.time()
       diff = end - start
       return diff / repeat
   
   def measure_fallback(repeat, a):
       # start bench
       start = time.time()
       results = []
       for i in range(repeat):
           results.append(a.tostype('default'))
       for result in results:
           result.wait_to_read()
       end = time.time()
       diff = end - start
       return diff / repeat
   
   def main():
       shape = (1000000, 128)
       dns = np.random.uniform(size=shape)
       context = mx.gpu(0)
       # context = mx.cpu()
       mx_dns1 = mx.nd.array(dns, ctx=context)
       mx_dns2 = mx.nd.array(dns, ctx=context)
       mx_dns3 = mx.nd.array(dns, ctx=context)
       for density in [0.01, 0.005, 0.001, 0.0005, 0.0001]:
           mx_csr = rand_ndarray(shape=shape, stype='csr', density=density).as_in_context(context)
           mx_csr_dns = mx_csr.tostype('default')
           mx_rsp = rand_ndarray(shape=shape, stype='row_sparse', density=density).as_in_context(context)
           mx_rsp_dns = mx_rsp.tostype('default')
           sparse_cost = 0.0
           dns_cost = 0.0
           mx.nd.waitall()
           #warmup
           check = mx.nd.sparse.add_n(mx_dns1, mx_csr, mx_rsp, mx_dns2, mx_dns3)
           dns1 = dns + mx_csr_dns.asnumpy() + mx_rsp_dns.asnumpy() + dns + dns
           assert_almost_equal(check.asnumpy(), dns1, atol=1e-5, rtol=1e-4)
           mx.nd.waitall()
           for i in range(20):
               sparse_cost += measure_cost(5, mx_dns1, mx_csr, mx_dns2, mx_rsp, mx_dns3)
               dns_cost += measure_cost(5, mx_dns1, mx_csr_dns, mx_dns2, mx_rsp_dns, mx_dns3)
           print("%.2f %%" % (density*100), dns_cost / sparse_cost)
   
   
   if __name__ == "__main__":
       main()
   ```

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