Java GPU Computing. Maarten Steur & Arjan Lamers

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1 Java GPU Computing Maarten Steur & Arjan Lamers

2 Overzicht OpenCL Simpel voorbeeld Casus Tips & tricks Vragen

3 Waarom GPU Computing

4 Afkortingen CPU, GPU, APU Khronos: OpenCL, OpenGL Nvidia: CUDA JogAmp JOCL, JavaCL, JOCL

5 GPU vergeleken met CPU Veel simpele cores Veel high bandwidth geheugen Intel core i7 GeForce GT 650M 8 cores 384 cores 180 Gflops 650 Gflops

6 Programmeer model Definieer stream (flow) Run in parallel

7 Gebruik Algorithme: Hoge Concurrency Partitioneerbaar Maar: Extra latency door on- en offloaden op de GPU Extra complexiteit

8 Componenten

9 Componenten

10 Voorbeeld (MacBook Pro) Platform Platform Platform Platform name: Apple profile: FULL_PROFILE spec version: OpenCL 1.2 vendor: Apple Device HD Graphics 4000 Driver:1.2(Aug :29:07) Max work group size:512 Global mem size: Local mem size: Max clock freq: 1200 Max compute units: 16 Device GeForce GT 650M Driver: b01 Max work group size:1024 Global mem size: Local mem size: Max clock freq: 900 Max compute units: 2 Device Intel(R) Core(TM) i7-3720qm 2.60GHz Driver:1.1 Max work group size:1024 Global mem size: Local mem size: Max clock freq: 2600 Max compute units: 8

11 Work & Memory

12 Application / Kernel Schrijf.cl files in C variant Kernels zijn de 'publieke' functies Java Bytecode Aparapi (OpenCL) RootBeer (CUDA)

13 Disclaimer

14 Parallel sort kernel void sort(global const float* in, global float* out, int size) { int i = get_global_id(0); // current thread float id = in[i]; int pos = 0; for (int j=0;j<size;j++) { float jd = in[j]; // in[j] < in[i]? bool smaller = (jx < ix) (jx == ix && j < i); pos += (smaller)?1:0; } out[pos] = id; }

15 Java GPU Computing CLContext globalcontext = CLContext.create(); CLDevice device = globalcontext.getmaxflopsdevice(type.gpu); CLContext context = CLContext.create(device); CLCommandQueue queue = device.createcommandqueue(); CLProgram program = context.createprogram( First8GpuComputing.class.getResourceAsStream("MyTask.cl") ).build(); Je kunt ook builden voor specifieke devices: build(device)

16 Java GPU Computing CLBuffer<FloatBuffer> inbuffer = context.createfloatbuffer( input.length, READ_ONLY); CLBuffer<FloatBuffer> outbuffer = context.createfloatbuffer( input.length, WRITE_ONLY); maptobuffer(inbuffer.getbuffer(), workload);

17 Java GPU Computing CLBuffer<FloatBuffer> inbuffer = context.createfloatbuffer( input.length, READ_ONLY); CLBuffer<FloatBuffer> outbuffer = context.createfloatbuffer( input.length, WRITE_ONLY); maptobuffer(inbuffer.getbuffer(), workload); CLKernel kernel = program.createclkernel("mytask"); kernel.putargs(inbuffer, outbuffer).putarg(workload.length);

18 Java GPU Computing CLBuffer<FloatBuffer> inbuffer = context.createfloatbuffer( input.length, READ_ONLY); CLBuffer<FloatBuffer> outbuffer = context.createfloatbuffer( input.length, WRITE_ONLY); maptobuffer(inbuffer.getbuffer(), workload); CLKernel kernel = program.createclkernel("mytask"); kernel.putargs(inbuffer, outbuffer).putarg(workload.length); queue.putwritebuffer(inbuffer, false).put1drangekernel(kernel, 0, globalworksize, localworksize).putreadbuffer(outbuffer, true); FloatBuffer output = outbuffer.getbuffer();

19 Praktijkcasus

20 Praktijk casus Rekeninstrument ter ondersteuning van de Programmatische Aanpak Stikstof.

21 Praktijk casus

22 Praktijk casus

23 Tips & tricks CL beheer getresourceasstream()? Java constanten #define Locale? Oops!

24 Tips & tricks Unit testen Aparte test kernels Test cases in batches kernel void testdifficultcalculation(const int testcount, global const double* distance, global double* results) { const int testid = get_global_id(0); if (testid < testcount) { results[testid] = difficultcalculation(distance[testid]); } }

25 Direct memory management -XX:MaxDirectMemorySize=??M ByteBuffer.allocateDirect(int capacity) Max 2GB per buffer Garbage collection te laat Getriggered door heap collection Handmatig vrijgeven ((sun.nio.ch.directbuffer) mybuffer).cleaner().clean(); VisualVM plugin voor direct buffers

26 GPU vs CPU GPU's checken minder dan CPU's Div by zero Out of bounds checks Test eerst op CPU

27 Portabiliteit OpenCL is portable, de performance niet Memory sizes verschillen Memory latencies verschillen Work group sizes verschillen Compute devices verschillen OpenCL implementatie verschillen Develop dus voor de productie hardware

28 Ten slotte Float vs Double Dubbele precisie Halve performance Double support optioneel

29 Conclusie

30 Conclusie Wanneer te gebruiken? Als performance echt nodig is Als probleem hoge concurrency heeft Als probleem partitioneerbaar is

31 Vragen? Setting up OpenCL test on Intel(R) Core(TM) i7-3720qm 2.60GHz Warming up OpenCL test [thread also had an error][thread also had an error] # # A fatal error has been detected by the Java Runtime Environment: # # SIGSEGV[thread also had an error] (0xb)[thread also had an error] [thread also had an error] at pc=0x ded70, pid=99851, tid=29475 # # JRE version: Java(TM) SE Runtime Environment (8.0_20-b26) (build 1.8.0_20-b26) # Java VM: Java HotSpot(TM) 64-Bit Server VM (25.20-b23 mixed mode bsd-amd64 compressed oops) # Problematic frame: # [thread also had an error] C [cl_kernels+0x1d70] sort_wrapper+0x1b0 # # Failed to write core dump. Core dumps have been disabled. To enable core dumping, try "ulimit -c unlimited" before starting Java again # # An error report file with more information is saved as: # /Users/arjanl/Documents/opencl/workspace/opencl-test/jogamp/hs_err_pid99851.log [thread also had an error] # # If you would like to submit a bug report, please visit: # #

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