mixbench benchmark OpenCL implementation

Four and a half months ago I posted an article about mixbench benchmark. This benchmark was used to assess performance of an artificial kernel with mixed compute and memory operations which corresponds to various operational intensities (Flops/byte ratios). The implementation was based on CUDA and therefore only NVidia GPUs could be used.

Now, I've ported the CUDA implementation to OpenCL and here I provide some performance numbers on an AMD R7-260X. Here is the output when using 128MB memory buffer:

mixbench-ocl (compute & memory balancing GPU microbenchmark)
Use "-h" argument to see available options
------------------------ Device specifications ------------------------
Device:              Bonaire
Driver version:      1800.11 (VM)
GPU clock rate:      1175 MHz
Total global mem:    1871 MB
Max allowed buffer:  1336 MB
OpenCL version:      OpenCL 2.0 AMD-APP (1800.11)
Total CUs:           14
-----------------------------------------------------------------------
Buffer size: 128MB
Workgroup size: 256
Workitem stride: NDRange
Loading kernel source file...
Precompilation of kernels... [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>]
--------------------------------------------------- CSV data --------------------------------------------------
Single Precision ops,,,,              Double precision ops,,,,              Integer operations,,,
Flops/byte, ex.time,  GFLOPS, GB/sec, Flops/byte, ex.time,  GFLOPS, GB/sec, Iops/byte, ex.time,   GIOPS, GB/sec
     0.000,  273.95,    0.00,  62.71,      0.000,  519.39,    0.00,  66.15,     0.000,  258.30,    0.00,  66.51
     0.065,  252.12,    4.26,  66.01,      0.032,  506.86,    2.12,  65.67,     0.065,  252.08,    4.26,  66.02
     0.133,  241.49,    8.89,  66.69,      0.067,  487.11,    4.41,  66.13,     0.133,  241.59,    8.89,  66.67
     0.207,  235.72,   13.67,  66.05,      0.103,  474.25,    6.79,  65.66,     0.207,  236.35,   13.63,  65.87
     0.286,  225.46,   19.05,  66.67,      0.143,  453.92,    9.46,  66.23,     0.286,  225.05,   19.08,  66.80
     0.370,  219.59,   24.45,  66.01,      0.185,  442.80,   12.12,  65.47,     0.370,  220.15,   24.39,  65.84
     0.462,  209.03,   30.82,  66.78,      0.231,  421.14,   15.30,  66.29,     0.462,  209.10,   30.81,  66.76
     0.560,  203.60,   36.92,  65.92,      0.280,  409.07,   18.37,  65.62,     0.560,  203.99,   36.85,  65.80
     0.667,  192.80,   44.55,  66.83,      0.333,  388.95,   22.09,  66.26,     0.667,  193.27,   44.44,  66.67
     0.783,  187.81,   51.46,  65.75,      0.391,  378.34,   25.54,  65.27,     0.783,  187.86,   51.44,  65.73
     0.909,  177.09,   60.63,  66.70,      0.455,  357.29,   30.05,  66.12,     0.909,  177.18,   60.60,  66.66
     1.048,  171.62,   68.82,  65.69,      0.524,  345.04,   34.23,  65.35,     1.048,  171.59,   68.83,  65.70
     1.200,  160.76,   80.15,  66.79,      0.600,  325.75,   39.55,  65.92,     1.200,  160.57,   80.24,  66.87
     1.368,  155.33,   89.86,  65.67,      0.684,  313.23,   44.56,  65.13,     1.368,  155.30,   89.88,  65.68
     1.556,  144.48,  104.05,  66.89,      0.778,  293.56,   51.21,  65.84,     1.556,  144.62,  103.95,  66.82
     1.765,  139.33,  115.60,  65.51,      0.882,  281.60,   57.20,  64.82,     1.765,  139.33,  115.60,  65.50
     2.000,  128.79,  133.40,  66.70,      1.000,  261.47,   65.70,  65.70,     2.000,  128.86,  133.32,  66.66
     2.267,  117.57,  155.26,  68.50,      1.133,  235.53,   77.50,  68.38,     2.267,  117.49,  155.36,  68.54
     2.571,  112.96,  171.10,  66.54,      1.286,  246.34,   78.46,  61.02,     2.571,  112.65,  171.57,  66.72
     2.923,  101.62,  200.77,  68.68,      1.462,  257.16,   79.33,  54.28,     2.923,  101.13,  201.72,  69.01
     3.333,   96.64,  222.22,  66.67,      1.667,  268.00,   80.13,  48.08,     3.333,   95.65,  224.51,  67.35
     3.818,   83.93,  268.65,  70.36,      1.909,  278.84,   80.86,  42.36,     3.818,   72.92,  309.24,  80.99
     4.400,   80.58,  293.16,  66.63,      2.200,  289.68,   81.55,  37.07,     4.400,   73.59,  321.00,  72.95
     5.111,   67.67,  364.96,  71.41,      2.556,  300.58,   82.16,  32.15,     5.111,   74.28,  332.49,  65.05
     6.000,   64.45,  399.83,  66.64,      3.000,  311.43,   82.75,  27.58,     6.000,   75.29,  342.26,  57.04
     7.143,   50.01,  536.76,  75.15,      3.571,  322.26,   83.30,  23.32,     7.143,   76.25,  352.04,  49.29
     8.667,   48.34,  577.52,  66.64,      4.333,  333.09,   83.81,  19.34,     8.667,   77.26,  361.33,  41.69
    10.800,   33.47,  866.12,  80.20,      5.400,  343.93,   84.29,  15.61,    10.800,   78.25,  370.48,  34.30
    14.000,   32.22,  932.99,  66.64,      7.000,  354.77,   84.74,  12.11,    14.000,   79.26,  379.32,  27.09
    19.333,   20.68, 1505.69,  77.88,      9.667,  376.91,   82.62,   8.55,    19.333,   80.27,  387.93,  20.07
    30.000,   19.37, 1663.32,  55.44,     15.000,  378.17,   85.18,   5.68,    30.000,   81.26,  396.41,  13.21
    62.000,   18.46, 1802.66,  29.08,     31.000,  389.93,   85.36,   2.75,    62.000,   33.57,  991.64,  15.99
       inf,   16.68, 2059.77,   0.00,        inf,  397.94,   86.34,   0.00,       inf,   33.54, 1024.43,   0.00
---------------------------------------------------------------------------------------------------------------

And here is "memory bandwidth" to "compute throughput" plot on the single precision floating point experiment results:

The source code of mixbench is freely provided, hosted at a github repository and you can find it at https://github.com/ekondis/mixbench. I would be happy to include results from other GPUs as well. Please try this tool and let me know about your extracted results and thoughts.

mixbench: A GPU benchmark for mixed compute/transfer bound kernels

I have just released mixbench on github. It is a benchmark tool which assesses performance bounds on GPUs (compute or memory bound) under mixed workloads. Unfortunately, it's currently implemented on CUDA so only NVidia GPUs can be used. The compute part can be SP Flops, DP Flops or Int ops and the memory part is global memory traffic. Running the multiple experiments in a wide range of operational intensity values allows to examine the performance of GPUs under different kernel characteristics.

Running the program under a GTX-480 gives the following output:

mixbench (compute & memory balancing GPU microbenchmark)
------------------------ Device specifications ------------------------
Device:              GeForce GTX 480
CUDA driver version: 5.50
GPU clock rate:      1401 MHz
Memory clock rate:   924 MHz
Memory bus width:    384 bits
WarpSize:            32
L2 cache size:       768 KB
Total global mem:    1535 MB
ECC enabled:         No
Compute Capability:  2.0
Total SPs:           480 (15 MPs x 32 SPs/MP)
Compute throughput:  1344.96 GFlops (theoretical single precision FMAs)
Memory bandwidth:    177.41 GB/sec
-----------------------------------------------------------------------
Total GPU memory 1610285056, free 1195106304
Buffer size: 256MB
Trade-off type:compute with global memory (block strided)
---- EXCEL data ----
Operations ratio ;  Single Precision ops ;;;  Double precision ops ;;;    Integer operations   
  compute/memory ;    Time;  GFLOPS; GB/sec;    Time;  GFLOPS; GB/sec;    Time;   GIOPS; GB/sec
       0/32      ; 240.531;    0.00; 142.85; 475.150;    0.00; 144.63; 240.205;    0.00; 143.04
       1/31      ; 233.548;    9.20; 142.52; 460.193;    4.67; 144.66; 233.484;    9.20; 142.56
       2/30      ; 225.249;   19.07; 143.01; 445.144;    9.65; 144.73; 225.235;   19.07; 143.02
       3/29      ; 218.552;   29.48; 142.48; 430.575;   14.96; 144.64; 218.745;   29.45; 142.35
       4/28      ; 210.345;   40.84; 142.93; 415.425;   20.68; 144.74; 210.091;   40.89; 143.10
       5/27      ; 203.132;   52.86; 142.72; 400.472;   26.81; 144.78; 203.275;   52.82; 142.62
       6/26      ; 194.468;   66.26; 143.56; 385.434;   33.43; 144.86; 194.314;   66.31; 143.67
       7/25      ; 187.470;   80.19; 143.19; 370.915;   40.53; 144.74; 187.475;   80.18; 143.18
       8/24      ; 175.115;   98.11; 147.16; 355.723;   48.30; 144.89; 175.132;   98.10; 147.14
       9/23      ; 171.760;  112.53; 143.78; 341.353;   56.62; 144.70; 171.920;  112.42; 143.65
      10/22      ; 163.397;  131.43; 144.57; 326.007;   65.87; 144.92; 163.252;  131.54; 144.70
      11/21      ; 155.797;  151.62; 144.73; 311.655;   75.80; 144.70; 155.814;  151.61; 144.71
      12/20      ; 146.573;  175.82; 146.51; 296.386;   86.95; 144.91; 146.662;  175.71; 146.42
      13/19      ; 138.853;  201.06; 146.93; 281.757;   99.08; 144.81; 138.941;  200.93; 146.83
      14/18      ; 129.727;  231.75; 148.98; 266.401;  112.86; 145.10; 129.744;  231.72; 148.97
      15/17      ; 121.228;  265.72; 150.57; 251.283;  128.19; 145.28; 121.339;  265.47; 150.43
      16/16      ; 120.065;  286.18; 143.09; 235.740;  145.75; 145.75; 120.122;  286.04; 143.02
      17/15      ; 111.357;  327.84; 144.64; 219.472;  166.34; 146.77; 111.528;  327.34; 144.41
      18/14      ; 106.430;  363.19; 141.24; 231.498;  166.98; 129.87; 106.541;  362.82; 141.10
      19/13      ;  96.118;  424.50; 145.22; 243.534;  167.54; 114.63;  96.494;  422.85; 144.66
      20/12      ;  89.602;  479.34; 143.80; 256.247;  167.61; 100.57;  89.642;  479.13; 143.74
      21/11      ;  81.976;  550.13; 144.08; 269.055;  167.61;  87.80;  83.091;  542.74; 142.15
      22/10      ;  76.066;  621.10; 141.16; 282.898;  167.00;  75.91;  76.068;  621.08; 141.15
      23/ 9      ;  65.631;  752.57; 147.24; 295.743;  167.01;  65.35;  76.895;  642.33; 125.67
      24/ 8      ;  60.809;  847.57; 141.26; 307.479;  167.62;  55.87;  80.099;  643.45; 107.24
      25/ 7      ;  52.032; 1031.82; 144.45; 321.449;  167.02;  46.76;  83.296;  644.53;  90.23
      26/ 6      ;  48.321; 1155.49; 133.33; 334.305;  167.02;  38.54;  86.519;  645.35;  74.46
      27/ 5      ;  49.519; 1170.90; 108.42; 347.157;  167.02;  30.93;  89.729;  646.19;  59.83
      28/ 4      ;  50.704; 1185.90;  84.71; 360.013;  167.02;  23.86;  92.891;  647.31;  46.24
      29/ 3      ;  52.024; 1197.09;  61.92; 372.867;  167.02;  17.28;  96.115;  647.94;  33.51
      30/ 2      ;  53.377; 1206.97;  40.23; 385.722;  167.02;  11.13;  99.328;  648.61;  21.62
      31/ 1      ;  53.437; 1245.80;  20.09; 397.203;  167.60;   5.41; 101.247;  657.52;  10.61
      32/ 0      ;  53.558; 1283.08;   0.00; 410.012;  167.60;   0.00; 102.494;  670.47;   0.00
--------------------

The results for single and double precision Flops are illustrated in the following charts:

% of peak SP Flops and memory bandwidth performance related with the operational intensity

% of peak DP Flops and memory bandwidth performance related with the operational intensity

Compute throughput (SP Flops) vs memory bandwidth

Compute throughput (DP Flops) vs memory bandwidth

Publication:

Since this work was initially part of published research please cite the following publication where applicable:

Konstantinidis, E.; Cotronis, Y., "A Practical Performance Model for Compute and Memory Bound GPU Kernels," Parallel, Distributed and Network-Based Processing (PDP), 2015 23rd Euromicro International Conference on , vol., no., pp.651,658, 4-6 March 2015
doi: 10.1109/PDP.2015.51
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7092788&isnumber=7092002