PyCUDA. Continued...

Transcription

1 PyCUDA Continued...

2 gpuarray Vector Types pycuda.gpuarray.vec All CUDA vector types are supported: float3, int3, long4, etc, Available as numpy data types Field names x, y, z, and w as in CUDA Construct using make_type function: e.g. make_float3(x, y, z)

3 Conditionals gpuarray.if_positive(criterion, then, else) Return an array like then, which, for the element at index i, contains then[i] if criterion>0, otherwise else[i]. gpuarray.maximum(a, b) Return the elementwise maximum of a and b gpuarray.minimum(a, b) Return elementwise minimum of a and b

4 Elementwise Kernel Avoid extra store-fetch cycles for elementwise math a b X X X + Y = Z

5 Elementwise Kernel Example import pycuda.gpuarray as gpuarray import pycuda.driver as cuda import pycuda.autoinit import numpy from pycuda.curandom import rand as curand a_gpu = curand((50,)) b_gpu = curand((50,)) from pycuda.elementwise import ElementwiseKernel lin_comb = ElementwiseKernel( "float a, float *x, float b, float *y, float *z", "z[i] = a*x[i] + b*y[i]", "linear_combination") c_gpu = gpuarray.empty_like(a_gpu) lin_comb(5, a_gpu, 6, b_gpu, c_gpu) import numpy.linalg as la assert la.norm((c_gpu - (5*a_gpu+6*b_gpu)).get()) < 1e-5

6 Reduction Kernel Example Example: A dot product calculation from pycuda.reduction import ReductionKernel from pycuda.curandom import rand as curand dot = ReductionKernel(dtype_out=numpy.float32, neutral="0", reduce_expr="a+b", map_expr="x[i]*y[i]", arguments="float *x, float *y") x = curand((1000*1000), dtype=numpy.float32) y = curand((1000*1000), dtype=numpy.float32) x_dot_y = dot(x, y).get() x_dot_y_cpu = numpy.dot(x.get(), y.get())

7 Parallel Scan / Prefix Sum X Y

8 Prefix Sum Example import pycuda.gpuarray as gpuarray import pycuda.driver as cuda import pycuda.autoinit import numpy as np from pycuda.scan import InclusiveScanKernel knl = InclusiveScanKernel( np.int32, "a+b" ) n = 2**20-2** host_data = np.random.randint( 0, 10, n ).astype( np.int32 ) dev_data = gpuarray.to_gpu( host data ) knl ( dev_data ) assert( dev_data.get() == np.cumsum ( hostdata, axis=0) ).all()

9 Custom Data Types Use your own data types in scan and reduction Define custom type in preamble tools.register_dtype(dtype, name)

10 Elementwise Math Functions Rounding and absolute value fabs, ceil, floor Exponentials, logarithms and roots exp, log, log10, sqrt Trigonometric functions sin, cos, tan, asin, acos, atan Hyperbolic functions sinh, cosh, tanh Floating point decomposition and assembly fmod, frexp, ldexp, modf

11 Random Number Generation curandom.rand(shape) Returns values in the range [0, 1) For more control in random number generation use the following curandom classes: XORWOWRandomNumberGenerator() Sobol32RandomNumberGenerator() ScrambledSobol32RandomNumberGenerator() Sobol64RandomNumberGenerator() ScrambledSobol64RandomNumberGenerator()

12 Monte Carlo Simulation Calculate PI Circle radius R and square with side length 2R Square area = (2R) 2 Circle area = πr 2 Ratio of the two areas is π/4 Pick N random points ~ Nπ/4 points fall within the circle Therefore: π=4m/n

13 Monte Carlo PI Example import pycuda.gpuarray as gpuarray import pycuda.driver as cuda import pycuda.autoinit import numpy from pycuda.curandom import XORWOWRandomNumberGenerator from pycuda.reduction import ReductionKernel rng = XORWOWRandomNumberGenerator() N = x_gpu = rng.gen_uniform((n,), dtype=numpy.float32) y_gpu = rng.gen_uniform((n,), dtype=numpy.float32) circle = ReductionKernel(numpy.dtype(numpy.float32), neutral="0", reduce_expr="a+b", map_expr="float((x[i]*x[i]+y[i]*y[i])<=1.0f)", arguments="float *x, float *y") result = 4.0 * circle(x_gpu, y_gpu).get() / N print 'Estimate for PI on GPU: {}'.format(result)

14 CUDA Programming Paradigm

15 Performance Analysis Tools CUDA_PROFILE=1 python calculate.py Generates cuda_profile_0.log file in same directory Provides breakdown of method executed, GPU time, CPU time and occupancy CUDA Visual Profiler Run computeprof executable Plots graphs for easy analysis

16 Event Timing Example import pycuda.autoinit import pycuda.driver as cuda import pycuda.curandom as curandom import numpy #create two timers start = cuda.event() end = cuda.event() #record the starting time start.record() #perform gpu computations for i in range(1000): curandom.rand(( ,)) #record finishing time end.record() end.synchronize() print "GPU time: %.2f seconds" % (start.time_till(end)*1e-3)

17 Occupancy pycuda.tools.occupancyrecord tb_per_mp How many thread blocks execute on each multi-processor limited_by What tb_per_mp is limited by. One of device, warps, regs, smem warps_per_mp How many warps execute on each multi-processor occupancy A float value between 0 and 1 indicating how much of each multi-processor's scheduling capability is occupied by the kernel

18 Important Hardware Properties Warp size Maximum block dimensions Max threads per block Max threads per multiprocessor Multiprocessor count

19 Query Hardware Example import pycuda.driver as drv drv.init() print "%d device(s) found." % drv.device.count() for ordinal in range(drv.device.count()): dev = drv.device(ordinal) print "Device #%d: %s" % (ordinal, dev.name()) print " Compute Capability: %d.%d" % dev.compute_capability() print " Total Memory: %s KB" % (dev.total_memory()//(1024)) atts = [(str(att), value) for att, value in dev.get_attributes().iteritems()] atts.sort() for att, value in atts: print " %s: %s" % (att, value)

20 Simple Optimisation Strategy Choose parameters based on your hardware constraits (e.g. block dimensions) Threads per block should be multiple of warp size Each multiprocessor should have enough active warps to hide instruction and memory latency Pad input data if necessary

21 N-Body Simulation

22 N-Body Example