def matrix_multiply(): # Configure array dimensions. Force an unequal data distribution. dims = [TOTALELEMS]*NDIM chunk = [TOTALELEMS/nprocs-1]*NDIM # Create a global array g_a and duplicate it to get g_b and g_c. g_a = ga.create(ga.C_DBL, dims, "array A", chunk) if not g_a: ga.error("create failed: A") if not me: print "Created Array A" g_b = ga.duplicate(g_a, "array B") g_c = ga.duplicate(g_a, "array C") if not g_b or not g_c: ga.eror("duplicate failed") if not me: print "Created Arrays B and C" # Initialize data in matrices a and b. if not me: print "Initializing matrix A and B" a = np.random.rand(*dims)*29 b = np.random.rand(*dims)*37 # Copy data to global arrays g_a and g_b. if not me: ga.put(g_a, a) ga.put(g_b, b) # Synchronize all processors to make sure everyone has data. ga.sync() # Determine which block of data is locally owned. Note that # the same block is locally owned for all GAs. lo,hi = ga.distribution(g_c) # Get the blocks from g_a and g_b needed to compute this block in # g_c and copy them into the local buffers a and b. a = ga.get(g_a, (lo[0],0), (hi[0],dims[0])) b = ga.get(g_b, (0,lo[1]), (dims[1],hi[1])) # Do local matrix multiplication and store the result in local # buffer c. Start by evaluating the transpose of b. btrns = b.transpose() # Multiply a and b to get c. c = np.dot(a,b) # Copy c back to g_c. ga.put(g_c, c, lo, hi) verify(g_a, g_b, g_c) # Deallocate arrays. ga.destroy(g_a) ga.destroy(g_b) ga.destroy(g_c)
def matrix_multiply(): # Configure array dimensions. Force an unequal data distribution. dims = [TOTALELEMS] * NDIM chunk = [TOTALELEMS / nprocs - 1] * NDIM # Create a global array g_a and duplicate it to get g_b and g_c. g_a = ga.create(ga.C_DBL, dims, "array A", chunk) if not g_a: ga.error("create failed: A") if not me: print "Created Array A" g_b = ga.duplicate(g_a, "array B") g_c = ga.duplicate(g_a, "array C") if not g_b or not g_c: ga.eror("duplicate failed") if not me: print "Created Arrays B and C" # Initialize data in matrices a and b. if not me: print "Initializing matrix A and B" a = np.random.rand(*dims) * 29 b = np.random.rand(*dims) * 37 # Copy data to global arrays g_a and g_b. if not me: ga.put(g_a, a) ga.put(g_b, b) # Synchronize all processors to make sure everyone has data. ga.sync() # Determine which block of data is locally owned. Note that # the same block is locally owned for all GAs. lo, hi = ga.distribution(g_c) # Get the blocks from g_a and g_b needed to compute this block in # g_c and copy them into the local buffers a and b. a = ga.get(g_a, (lo[0], 0), (hi[0], dims[0])) b = ga.get(g_b, (0, lo[1]), (dims[1], hi[1])) # Do local matrix multiplication and store the result in local # buffer c. Start by evaluating the transpose of b. btrns = b.transpose() # Multiply a and b to get c. c = np.dot(a, b) # Copy c back to g_c. ga.put(g_c, c, lo, hi) verify(g_a, g_b, g_c) # Deallocate arrays. ga.destroy(g_a) ga.destroy(g_b) ga.destroy(g_c)
def verify_using_ga(g_a, g_b, g_c): g_v = ga.duplicate(g_c) ga.gemm(False,False,N,N,N,1,g_a,g_b,0,g_v) c = ga.access(g_c) v = ga.access(g_v) if c is not None: val = int(np.abs(np.sum(c-v))>0.0001) else: val = 0 val = ga.gop_add(val) ga.destroy(g_v) return val == 0
def TRANSPOSE1D(): # Configure array dimensions. Force an unequal data distribution. dims = [nprocs * TOTALELEMS + nprocs / 2] chunk = [TOTALELEMS] # minimum data on each process # create a global array g_a and duplicate it to get g_b g_a = ga.create(ga.C_INT, dims, "array A", chunk) if not g_a: ga.error("create failed: A") if not me: print "Created Array A" g_b = ga.duplicate(g_a, "array B") if not g_b: ga.error("duplicate failed") if not me: print "Created Array B" # initialize data in g_a if not me: print "Initializing matrix A" ga.put(g_a, np.arange(dims[0], dtype=np.int32)) # Synchronize all processors to guarantee that everyone has data # before proceeding to the next step. ga.sync() # Start initial phase of inversion by inverting the data held locally on # each processor. Start by finding out which data each processor owns. lo, hi = ga.distribution(g_a) # Get locally held data and copy it into local buffer a a = ga.get(g_a, lo, hi) # Invert data locally b = a[::-1] # Invert data globally by copying locally inverted blocks into # their inverted positions in the GA ga.put(g_b, b, dims[0] - hi[0], dims[0] - lo[0]) # Synchronize all processors to make sure inversion is complete ga.sync() # Check to see if inversion is correct if not me: verify(g_a, g_b) # Deallocate arrays ga.destroy(g_a) ga.destroy(g_b)
def TRANSPOSE1D(): # Configure array dimensions. Force an unequal data distribution. dims = [nprocs*TOTALELEMS + nprocs/2] chunk = [TOTALELEMS] # minimum data on each process # create a global array g_a and duplicate it to get g_b g_a = ga.create(ga.C_INT, dims, "array A", chunk) if not g_a: ga.error("create failed: A") if not me: print "Created Array A" g_b = ga.duplicate(g_a, "array B") if not g_b: ga.error("duplicate failed") if not me: print "Created Array B" # initialize data in g_a if not me: print "Initializing matrix A" ga.put(g_a, np.arange(dims[0], dtype=np.int32)) # Synchronize all processors to guarantee that everyone has data # before proceeding to the next step. ga.sync() # Start initial phase of inversion by inverting the data held locally on # each processor. Start by finding out which data each processor owns. lo,hi = ga.distribution(g_a) # Get locally held data and copy it into local buffer a a = ga.get(g_a, lo, hi) # Invert data locally b = a[::-1] # Invert data globally by copying locally inverted blocks into # their inverted positions in the GA ga.put(g_b, b, dims[0]-hi[0], dims[0]-lo[0]) # Synchronize all processors to make sure inversion is complete ga.sync() # Check to see if inversion is correct if not me: verify(g_a, g_b) # Deallocate arrays ga.destroy(g_a) ga.destroy(g_b)
def verify(g_a, g_b, g_c): g_chk = ga.duplicate(g_a, "array check") if not g_chk: ga.error("duplicate failed") ga.sync() ga.gemm(False, False, TOTALELEMS, TOTALELEMS, TOTALELEMS, 1.0, g_a, g_b, 0.0, g_chk) ga.sync() ga.add(g_c, g_chk, g_chk, 1.0, -1.0) rchk = ga.dot(g_chk, g_chk) if not me: print "Normed difference in matrices: %12.4f" % rchk if not (-TOLERANCE < rchk < TOLERANCE): ga.error("Matrix multiply verify failed") else: print "Matrix Multiply OK" ga.destroy(g_chk)
def verify(g_a, g_b, g_c): g_chk = ga.duplicate(g_a, "array check") if not g_chk: ga.error("duplicate failed") ga.sync() ga.gemm(False, False, TOTALELEMS, TOTALELEMS, TOTALELEMS, 1.0, g_a, g_b, 0.0, g_chk); ga.sync() ga.add(g_c, g_chk, g_chk, 1.0, -1.0) rchk = ga.dot(g_chk, g_chk) if not me: print "Normed difference in matrices: %12.4f" % rchk if not (-TOLERANCE < rchk < TOLERANCE): ga.error("Matrix multiply verify failed") else: print "Matrix Multiply OK" ga.destroy(g_chk)