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ParallelAlgorithms.py
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ParallelAlgorithms.py
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import pycuda.autoinit
import pycuda.driver as drv
import pycuda.gpuarray as gpuarray
import pycuda.cumath
import skcuda.misc
import skcuda.linalg as linalg
import numpy as np
import matplotlib.pyplot as plt
import time
import scipy.io as sio
from CSMSSMTools import *
from pycuda.compiler import SourceModule
bitonicSort_ = None
getSumSquares_ = None
finishCSM_ = None
CSM = None
def initParallelAlgorithms():
global bitonicSort_
fin = open("ParallelAlgorithms/bitonicSort.cu")
mod = SourceModule(fin.read())
fin.close()
bitonicSort_ = mod.get_function("bitonicSort")
global finishCSM_
global getSumSquares_
fin = open("ParallelAlgorithms/CSMHelper.cu")
mod = SourceModule(fin.read())
fin.close()
finishCSM_ = mod.get_function("finishCSM")
getSumSquares_ = mod.get_function("getSumSquares")
#Run each of the algorithms on dummy data so that they're pre-compiled
#1) Bitonic Sort
X = np.random.randn(16, 16)
N = np.int32(16)
NPow2 = N
NThreads = N/2
XG = gpuarray.to_gpu(X)
bitonicSort_(XG, N, NPow2, block=(NThreads, 1, 1), grid=(X.shape[0], 1), shared=4*NPow2)
linalg.init()
#2) Other primitive operations
NegXDotX = linalg.dot(XG, XG)
XPlusX = skcuda.misc.add(XG, XG)
XSqr = skcuda.misc.multiply(XG, XG)
XSqr = skcuda.misc.sum(XSqr, 1)
XPlusCol = skcuda.misc.add_matvec(XG, XSqr, 0)
def roundUpPow2(x):
return np.int32(int(2**np.ceil(np.log2(float(x)))))
def bitonicSort(XG):
N = np.int32(XG.shape[1])
NPow2 = np.int32(2**np.ceil(np.log2(N)))
N2 = NPow2/2
NThreads = min(N2, 512)
bitonicSort_(XG, N, NPow2, block=(NThreads, 1, 1), grid=(XG.shape[0], 1), shared=4*NPow2)
def getCSMGPU(XG, YG):
tbegin = time.time()
GPUNeg2 = gpuarray.to_gpu(np.array([-2.0], dtype=np.float32))
YGT = linalg.transpose(YG)
XSqr = skcuda.misc.multiply(XG, XG)
XSqr = skcuda.misc.sum(XSqr, 1)
YSqr = skcuda.misc.multiply(YG, YG)
YSqr = skcuda.misc.sum(YSqr, 1)
C = linalg.dot(XG, YGT)
C = skcuda.misc.multiply(GPUNeg2, C)
skcuda.misc.add_matvec(C, XSqr, 0, C)
skcuda.misc.add_matvec(C, YSqr, 1, C)
return C
def getCSMGPU2(XG, YG):
#Step 1: Sum of squares across rows
dim = np.int32(XG.shape[1])
dimpow2 = roundUpPow2(dim)
NThreads = np.int32(min(dimpow2, 512))
XSqr = gpuarray.empty(XG.shape[0], np.float32)
YSqr = gpuarray.empty(YG.shape[0], np.float32)
getSumSquares_(XG, XSqr, dim, dimpow2, block=(NThreads, 1, 1), grid=(XG.shape[0], 1), shared=4*dimpow2)
getSumSquares_(YG, YSqr, dim, dimpow2, block=(NThreads, 1, 1), grid=(YG.shape[0], 1), shared=4*dimpow2)
#Step 2: Do multiplication part
YGT = linalg.transpose(YG)
CSM = linalg.dot(XG, YGT)
#Step 3: Add everything together
Mp = np.array(XG.shape[0], dtype=np.int32)
Np = np.array(YG.shape[0], dtype=np.int32)
MPow2 = roundUpPow2(XG.shape[0])
NThreads = min(MPow2, 512)
#CSM is N x M
finishCSM_(CSM, XSqr, YSqr, Np, Mp, MPow2, block=(NThreads, 1, 1), grid=(YG.shape[0], 1))
return (CSM, XSqr, YSqr)
def testBitonicSort(N, doPlot = False):
X = np.array(np.random.rand(N*400, N), dtype=np.float32)
tic = time.time()
XG = gpuarray.to_gpu(X)
toc = time.time()
print("Elapsed memcopy time: %g"%(toc-tic))
tic = time.time()
bitonicSort(XG)
skcuda.misc.add(XG, XG)
toc = time.time()
GPUTime = toc-tic
tic = time.time()
X2 = np.sort(X, 1)
toc = time.time()
CPUTime = toc-tic
tic = time.time()
J = np.argpartition(X, N/10, 1)
toc = time.time()
print("Elapsed Time Partition: %g"%(toc-tic))
print("N = %i"%N)
print("Elapsed Time CPU: %g"%CPUTime)
print("Elapsed Time GPU: %g (Ratio %.3g)"%(GPUTime, CPUTime/GPUTime))
print("AllClose: ", np.allclose(np.sort(X, 1), XG.get()))
if doPlot:
plt.subplot(121)
plt.imshow(X, interpolation = 'none')
plt.subplot(122)
plt.imshow(XG.get(), interpolation = 'none')
plt.show()
return (CPUTime, GPUTime)
def testBitonicSortTimeRatios(sizes, NTrials):
np.random.seed(100)
CPUTimes = np.zeros((len(sizes), NTrials))
GPUTimes = np.zeros((len(sizes), NTrials))
for i in range(len(sizes)):
N = sizes[i]
for t in range(NTrials):
(CPUTimes[i, t], GPUTimes[i, t]) = testBitonicSort(N)
sio.savemat("Timings.mat", {"CPUTimes":CPUTimes, "GPUTimes":GPUTimes})
def testCSMTimes():
N = 800
K = 1
X = np.array(np.random.randn(N, 25*25), dtype = np.float32)
Y = np.array(np.random.randn(N*K, 25*25), dtype=np.float32)
XG = gpuarray.to_gpu(X)
YG = gpuarray.to_gpu(Y)
tic = time.time()
CSM = getCSM(X, Y)
CPUTime = time.time() - tic
tic = time.time()
#(CSMG, XSqr, YSqr) = getCSMGPU2(XG, YG)
CSMG = getCSMGPU(XG, YG)
CSMG = CSMG.get()
GPUTime = time.time() - tic
#plt.plot(XSqr.get(), np.sum(X**2, 1), '.')
#plt.show()
print("CPUTime: %g"%CPUTime)
print("GPUTime: %g"%GPUTime)
plt.subplot(131)
plt.imshow(CSM, interpolation = 'none', cmap = 'afmhot')
plt.subplot(132)
plt.imshow(CSMG, interpolation = 'none', cmap = 'afmhot')
plt.subplot(133)
plt.imshow(CSM - CSMG, interpolation = 'none', cmap = 'spectral')
plt.show()
if __name__ == '__main__2':
np.random.seed(100)
initParallelAlgorithms()
t = testCSM(1000, 1000, 60, doPlot = False)
print("Return time: %g"%(time.time() - t))
if __name__ == '__main__':
initParallelAlgorithms()
#testBitonicSort(800, False)
testCSMTimes()