Ishitori opened a new issue #11337: Slow perfromance of argmax compared to max on GPU URL: https://github.com/apache/incubator-mxnet/issues/11337 ## Description Slyforce@ [has reported](https://discuss.mxnet.io/t/mx-nd-argmax-slow-on-gpu-with-high-reduction-dimensions/1231) a slow performance of argmax compared to max. I've tried it on EC2 machine and confirm the finding - on high dimensions difference between max and argmax looks suspiciously high. Haibin suspects the code is not parallelized well. ## Environment info (Required) ``` ----------Python Info---------- Version : 3.6.4 Compiler : GCC 7.2.0 Build : ('default', 'Jan 16 2018 18:10:19') Arch : ('64bit', '') ------------Pip Info----------- Version : 10.0.1 Directory : /home/ubuntu/.virtualenvs/so_question2/lib/python3.6/site-packages/pip ----------MXNet Info----------- Version : 1.2.0 Directory : /home/ubuntu/.virtualenvs/so_question2/lib/python3.6/site-packages/mxnet Commit Hash : 297c64fd2ee404612aa3ecc880b940fb2538039c ----------System Info---------- Platform : Linux-4.4.0-1054-aws-x86_64-with-debian-stretch-sid system : Linux node : ip-172-31-84-4 release : 4.4.0-1054-aws version : #63-Ubuntu SMP Wed Mar 28 19:42:42 UTC 2018 ----------Hardware Info---------- machine : x86_64 processor : x86_64 Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 4 On-line CPU(s) list: 0-3 Thread(s) per core: 2 Core(s) per socket: 2 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz Stepping: 1 CPU MHz: 2699.984 CPU max MHz: 3000.0000 CPU min MHz: 1200.0000 BogoMIPS: 4600.16 Hypervisor vendor: Xen Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 46080K NUMA node0 CPU(s): 0-3 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single retpoline kaiser fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0017 sec, LOAD: 0.4570 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0665 sec, LOAD: 0.0495 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 1.3137 sec, LOAD: 0.3615 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0214 sec, LOAD: 0.1381 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0029 sec, LOAD: 0.1154 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0025 sec, LOAD: 0.0361 sec. ``` Package used (Python/R/Scala/Julia): Python 3 ## Minimum reproducible example ``` import time import mxnet as mx def max(x, ctx): return mx.nd.max(x, axis=1) def argmax(x, ctx): return mx.nd.argmax(x, axis=1) def measure_time(func, iters, inputs, ctx): begin = time.time() for i in range(iters): result = func(inputs[i,:,:], ctx=ctx) result.wait_to_read() return time.time() - begin ctx = mx.gpu() batch_size = 32 iterations = 500 for reduction_dimension in [25, 50, 100, 1000, 10000, 100000]: print('reduction dimension: {}'.format(reduction_dimension)) inputs = mx.nd.random_uniform(0, 100, shape=(iterations, batch_size, reduction_dimension), ctx=ctx) t = measure_time(argmax, iterations, inputs, ctx) print("argmax took {} seconds".format(t)) t = measure_time(max, iterations, inputs, ctx) print("max took {} seconds".format(t)) print('') ``` If I run it I get: ``` reduction dimension: 25 argmax took 0.15082168579101562 seconds max took 0.13338756561279297 seconds reduction dimension: 50 argmax took 0.17458558082580566 seconds max took 0.15340065956115723 seconds reduction dimension: 100 argmax took 0.26195740699768066 seconds max took 0.19835686683654785 seconds reduction dimension: 1000 argmax took 1.2869455814361572 seconds max took 0.7969081401824951 seconds reduction dimension: 10000 argmax took 11.152163982391357 seconds max took 7.157193422317505 seconds reduction dimension: 100000 argmax took 114.18031907081604 seconds max took 70.90202450752258 seconds ``` ## Steps to reproduce 1. Run the script above 2. See big difference in numbers.
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