def gpu_sdot(a,b):
assert a.size == b.size
assert a.shape[0] == b.shape[1]
cublas.cublasInit()
cublas.cublasFree(0)
d_X = Linear(a.shape).from_numpy(a)
d_Y = Linear(b.shape).from_numpy(b)
gpu_result = cublas.cublasSdot(a.shape[1], d_X.ref, 1, d_Y.ref, 1)
cuda.cudaThreadSynchronize()
cublas.cublasShutdown()
return gpu_result
def gpu_sdot(a, b):
assert a.size == b.size
assert a.shape[0] == b.shape[1]
cublas.cublasInit()
cublas.cublasFree(0)
d_X = Linear(a.shape).from_numpy(a)
d_Y = Linear(b.shape).from_numpy(b)
gpu_result = cublas.cublasSdot(a.shape[1], d_X.ref, 1, d_Y.ref, 1)
cuda.cudaThreadSynchronize()
cublas.cublasShutdown()
return gpu_result
def gpu_sgemm(a,b, alpha=1):
""" Single Precision Matrix Multiplication on GPU, expects two, two-dimensional numpy arrays as input. Arrays must be such that a.shape[1] == b.shape[0]. Optionally specify alpha for scalar multiplication"""
# init cublas
cublasInit()
assert a.shape[1] == b.shape[0]
c_shape = (a.shape[0], b.shape[1])
# allocate device matrices from host
dA = Linear(a.shape, order='F').from_numpy(a)
dB = Linear(b.shape, order='F').from_numpy(b)
dC = Linear(c_shape, order='F')
# transpose a/b ? t = yes, n = no
transa = 'n'
transb = 'n'
# compute with CUBLAS
cublasSgemm( transa, transb, a.shape[0], b.shape[1], a.shape[1], alpha, dA.ref, a.shape[0], dB.ref, b.shape[0], 0, dC.ref, a.shape[0] )
cudaThreadSynchronize()
# shutdown
cublasShutdown()
return dC.to_numpy()
def gpu_sgemm(a, b, alpha=1):
""" Single Precision Matrix Multiplication on GPU, expects two, two-dimensional numpy arrays as input. Arrays must be such that a.shape[1] == b.shape[0]. Optionally specify alpha for scalar multiplication"""
# init cublas
cublasInit()
assert a.shape[1] == b.shape[0]
c_shape = (a.shape[0], b.shape[1])
# allocate device matrices from host
dA = Linear(a.shape, order='F').from_numpy(a)
dB = Linear(b.shape, order='F').from_numpy(b)
dC = Linear(c_shape, order='F')
# transpose a/b ? t = yes, n = no
transa = 'n'
transb = 'n'
# compute with CUBLAS
cublasSgemm(transa, transb, a.shape[0], b.shape[1], a.shape[1], alpha,
dA.ref, a.shape[0], dB.ref, b.shape[0], 0, dC.ref, a.shape[0])
cudaThreadSynchronize()
# shutdown
cublasShutdown()
return dC.to_numpy()
