Index: MultiSource/Benchmarks/CMakeLists.txt =================================================================== --- MultiSource/Benchmarks/CMakeLists.txt +++ MultiSource/Benchmarks/CMakeLists.txt @@ -19,6 +19,7 @@ add_subdirectory(nbench) add_subdirectory(sim) add_subdirectory(DOE-ProxyApps-C) +add_subdirectory(Rodinia) if((NOT "${TARGET_OS}" STREQUAL "Darwin") OR (NOT "${ARCH}" STREQUAL "ARM")) add_subdirectory(TSVC) Index: MultiSource/Benchmarks/Rodinia/CMakeLists.txt =================================================================== --- MultiSource/Benchmarks/Rodinia/CMakeLists.txt +++ MultiSource/Benchmarks/Rodinia/CMakeLists.txt @@ -0,0 +1,2 @@ +add_subdirectory(backprop) + Index: MultiSource/Benchmarks/Rodinia/Makefile =================================================================== --- MultiSource/Benchmarks/Rodinia/Makefile +++ MultiSource/Benchmarks/Rodinia/Makefile @@ -0,0 +1,3 @@ +LEVEL = ../../.. +PARALLEL_DIRS := backprop +include $(LEVEL)/Makefile.programs Index: MultiSource/Benchmarks/Rodinia/backprop/CMakeLists.txt =================================================================== --- MultiSource/Benchmarks/Rodinia/backprop/CMakeLists.txt +++ MultiSource/Benchmarks/Rodinia/backprop/CMakeLists.txt @@ -0,0 +1,6 @@ +set(PROG backprop) +list(APPEND LDFLAGS -lm) + + +set(WORKDIR ${CMAKE_CURRENT_BINARY_DIR}) +llvm_multisource() Index: MultiSource/Benchmarks/Rodinia/backprop/Makefile =================================================================== --- MultiSource/Benchmarks/Rodinia/backprop/Makefile +++ MultiSource/Benchmarks/Rodinia/backprop/Makefile @@ -0,0 +1,7 @@ +LEVEL = ../../../.. +LIBS += -lm +LDFLAGS += -lm +PROG = backprop + + +include ../../Makefile.multisrc Index: MultiSource/Benchmarks/Rodinia/backprop/backprop.h =================================================================== --- MultiSource/Benchmarks/Rodinia/backprop/backprop.h +++ MultiSource/Benchmarks/Rodinia/backprop/backprop.h @@ -0,0 +1,35 @@ +#ifndef _BACKPROP_H_ +#define _BACKPROP_H_ +#include +#include +#include +#include +#define BIGRND 0x7fffffff + +#define SEED 7 +#define ETA 0.3 // eta value +#define MOMENTUM 0.3 // momentum value + + +#define LAYERSIZE 555365 + +#define HIDDEN_SIZE 16 +#define OUTPUT_SIZE 1 + + + +void bpnn_train_kernel( + int input_n, + int hidden_n, + int output_n, + float * __restrict__ input_units, + float * __restrict__ hidden_units, + float * __restrict__ output_units, + float * __restrict__ hidden_delta, + float * __restrict__ output_delta, + float * __restrict__ target, + float input_weights[LAYERSIZE+1][HIDDEN_SIZE+1], + float hidden_weights[HIDDEN_SIZE+1][OUTPUT_SIZE+1], + float input_prev_weights[LAYERSIZE+1][HIDDEN_SIZE+1], + float hidden_prev_weights[HIDDEN_SIZE+1][OUTPUT_SIZE+1]); +#endif Index: MultiSource/Benchmarks/Rodinia/backprop/backprop.reference_output =================================================================== --- MultiSource/Benchmarks/Rodinia/backprop/backprop.reference_output +++ 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+0.314574 +0.261607 +0.087673 +0.997552 +0.954609 +0.214325 +0.059552 +0.942043 +0.834706 +0.521894 +0.858589 +0.750655 +0.950253 +0.862404 +0.803110 +0.303805 +0.533823 +0.919780 +0.182466 +0.875231 +0.010439 +0.479513 +0.695518 +0.023757 +0.358445 +0.966620 +0.909069 +0.858773 +exit 0 Index: MultiSource/Benchmarks/Rodinia/backprop/backprop_kernel.c =================================================================== --- MultiSource/Benchmarks/Rodinia/backprop/backprop_kernel.c +++ MultiSource/Benchmarks/Rodinia/backprop/backprop_kernel.c @@ -0,0 +1,129 @@ +/* + ****************************************************************** + * HISTORY + * 15-Oct-94 Jeff Shufelt (js), Carnegie Mellon University + * Prepared for 15-681, Fall 1994. + * Modified by Shuai Che + * 28-May-2018: Modified by Pankaj Kukreja, + * Indian Institute of Technology Hyderabad, India + ****************************************************************** + */ + +#include "backprop.h" + +#define ABS(x) (((x) > 0.0) ? (x) : (-(x))) + +void bpnn_train_kernel( + int input_n, + int hidden_n, + int output_n, + float * __restrict__ input_units, + float * __restrict__ hidden_units, + float * __restrict__ output_units, + float * __restrict__ hidden_delta, + float * __restrict__ output_delta, + float * __restrict__ target, + float input_weights[LAYERSIZE+1][HIDDEN_SIZE+1], + float hidden_weights[HIDDEN_SIZE+1][OUTPUT_SIZE+1], + float input_prev_weights[LAYERSIZE+1][HIDDEN_SIZE+1], + float hidden_prev_weights[HIDDEN_SIZE+1][OUTPUT_SIZE+1]) +{ + int in, hid, out; + float out_err, hid_err; + + in = input_n; + hid = hidden_n; + out = output_n; + + // bpnn_layerforward(input_units, hidden_units, in, hid, input_weights); + { + float sum; + int j, k; + + input_units[0] = 1.0; + for (j = 1; j <= hid; j++) { + sum = 0.0; + for (k = 0; k <= in; k++) { + sum += input_weights[k][j] * input_units[k]; + } + hidden_units[j] = (1.0 / (1.0 + exp(-sum))); + } + } + + // bpnn_layerforward(hidden_units, output_units, hid, out, hidden_weights); + { + float sum; + int j, k; + hidden_units[0] = 1.0; + for (j = 1; j <= out; j++) { + sum = 0.0; + for (k = 0; k <= hid; k++) { + sum += hidden_weights[k][j] * hidden_units[k]; + } + output_units[j] = (1.0 / (1.0 + exp(-sum))); + } + } + + // bpnn_output_error(output_delta, target, output_units, out, &out_err); + { + int j; + float o, t, errsum; + errsum = 0.0; + for (j = 1; j <= out; j++) { + o = output_units[j]; + t = target[j]; + output_delta[j] = o * (1.0 - o) * (t - o); + errsum += ABS(output_delta[j]); + } + out_err = errsum; + } + + // bpnn_hidden_error(hidden_delta, hid, output_delta, out, hidden_weights, hidden_units, &hid_err); + { + int j, k; + float sum, errsum; + errsum = 0.0; + for (j = 1; j <= hid; j++) { + sum = 0.0; + for (k = 1; k <= out; k++) { + sum += output_delta[k] * hidden_weights[j][k]; + } + hidden_delta[j] = hidden_units[j] * (1.0 - hidden_units[j]) * sum; + errsum += ABS(hidden_delta[j]); + } + hid_err = errsum; + } + + // bpnn_adjust_weights(output_delta, out, hidden_units, hid, hidden_weights, hidden_prev_weights); + { + float new_dw; + int k, j; + hidden_units[0] = 1.0; + + for (j = 1; j <= out; j++) { + for (k = 0; k <= hid; k++) { + new_dw = ((ETA * output_delta[j] * hidden_units[k]) + + (MOMENTUM * hidden_prev_weights[k][j])); + hidden_weights[k][j] += new_dw; + hidden_prev_weights[k][j] = new_dw; + } + } + } + + // bpnn_adjust_weights(hidden_delta, hid, input_units, in, input_weights, input_prev_weights); + { + float new_dw; + int k, j; + input_units[0] = 1.0; + + for (j = 1; j <= hid; j++) { + for (k = 0; k <= in; k++) { + new_dw = ((ETA * hidden_delta[j] * input_units[k]) + (MOMENTUM * input_prev_weights[k][j])); + input_weights[k][j] += new_dw; + input_prev_weights[k][j] = new_dw; + } + } + } + + printf("Output Error: %.6f\nHidden Error: %.6f\n", out_err, hid_err); +} \ No newline at end of file Index: MultiSource/Benchmarks/Rodinia/backprop/main.c =================================================================== --- MultiSource/Benchmarks/Rodinia/backprop/main.c +++ MultiSource/Benchmarks/Rodinia/backprop/main.c @@ -0,0 +1,265 @@ +#include "backprop.h" +#include +#include +#include +#include +#include + +// GAP Between array index, which we want to compare +// Otherwise very big reference output +#define GAP 100 + +int input_n = 0; /* number of input units */ +int hidden_n = 0; /* number of hidden units */ +int output_n = 0; /* number of output units */ + +float *__restrict__ input_units; +float *__restrict__ hidden_units; +float *__restrict__ output_units; +float *__restrict__ hidden_delta; +float *__restrict__ output_delta; +float *__restrict__ target; + +float input_weights[LAYERSIZE+1][HIDDEN_SIZE+1]; +float hidden_weights[HIDDEN_SIZE+1][OUTPUT_SIZE+1]; +float input_prev_weights[LAYERSIZE+1][HIDDEN_SIZE+1]; +float hidden_prev_weights[HIDDEN_SIZE+1][OUTPUT_SIZE+1]; +void bpnn_internal_create(int n_in, int n_hidden, int n_out); +//////////////////////////////////////////////////////////////////////////////// +// Program main +//////////////////////////////////////////////////////////////////////////////// + + +void bpnn_dump() { + int n1, n2, n3, i, j; + + n1 = input_n; + n2 = hidden_n; + n3 = output_n; + fflush(stdout); + + for (i = 0; i <= n1; i++) { + for (j = 0; j <= n2; j++) { + if((i*n2+i+j) % GAP == 0) + { + printf("%.6f\n", input_weights[i][j]); + } + } + } + + + for (i = 0; i <= n2; i++) { + for (j = 0; j <= n3; j++) { + if((i*n3+i+j) % GAP == 0) + { + printf("%.6f\n", hidden_weights[i][j]); + } + } + } + return; +} + + +/*** Creates a new fully-connected network from scratch, + with the given numbers of input, hidden, and output units. + Threshold units are automatically included. All weights are + randomly initialized. + + Space is also allocated for temporary storage (momentum weights, + error computations, etc). +***/ +void bpnn_create(int n_in, int n_hidden, int n_out) { + + bpnn_internal_create(n_in, n_hidden, n_out); + +#ifdef INITZERO + // bpnn_zero_weights(input_weights, n_in, n_hidden); + { + int i, j; + for (i = 0; i <= n_in; i++) { + for (j = 0; j <= n_hidden; j++) { + input_weights[i][j] = 0.0; + } + } + } +#else + // void bpnn_randomize_weights(float *w, int m, int n) + // bpnn_randomize_weights(input_weights, n_in, n_hidden); + { + int i, j; + for (i = 0; i <= n_in; i++) { + for (j = 0; j <= n_hidden; j++) { + input_weights[i][j] = (float)rand() / RAND_MAX; + } + } + } + +#endif + // bpnn_randomize_weights(hidden_weights, n_hidden, n_out); + { + int i, j; + for (i = 0; i <= n_hidden; i++) { + for (j = 0; j <= n_out; j++) { + hidden_weights[i][j] = (float)rand() / RAND_MAX; + } + } + } + + // bpnn_zero_weights(input_prev_weights, n_in, n_hidden); + { + int i, j; + for (i = 0; i <= n_in; i++) { + for (j = 0; j <= n_hidden; j++) { + input_prev_weights[i][j] = 0.0; + } + } + } + // bpnn_zero_weights(hidden_prev_weights, n_hidden, n_out); + { + int i, j; + for (i = 0; i <= n_hidden; i++) { + for (j = 0; j <= n_out; j++) { + hidden_prev_weights[i][j] = 0.0; + } + } + } + + // bpnn_randomize_row(target, n_out); + { + int i; + for (i = 0; i <= n_out; i++) { + target[i] = 0.1; + } + } +} + + + +void bpnn_free() { + free(input_units); + free(hidden_units); + free(output_units); + free(hidden_delta); + free(output_delta); + free(target); +} + + +void bpnn_internal_create(int n_in, int n_hidden, int n_out) { + + input_n = n_in; + hidden_n = n_hidden; + output_n = n_out; + + input_units = + /*alloc_1d_dbl(n_in + 1);*/ (float *)malloc(((n_in + 1) * sizeof(float))); + if (input_units == NULL) { + fprintf(stderr, "Couldn't allocate array of floats to input_units\n"); + exit (EXIT_FAILURE); + } + + hidden_units = /*alloc_1d_dbl(n_hidden + 1); */ (float *)malloc( + ((n_hidden + 1) * sizeof(float))); + if (hidden_units == NULL) { + fprintf(stderr, "Couldn't allocate array of floats to hidden_units\n"); + exit (EXIT_FAILURE); + } + + output_units = /* alloc_1d_dbl(n_out + 1);*/ (float *)malloc( + ((n_out + 1) * sizeof(float))); + if (output_units == NULL) { + fprintf(stderr, "Couldn't allocate array of floats to output_units\n"); + exit (EXIT_FAILURE); + } + + hidden_delta = /*alloc_1d_dbl(n_hidden + 1);*/ (float *)malloc( + ((n_hidden + 1) * sizeof(float))); + if (hidden_delta == NULL) { + fprintf(stderr, "Couldn't allocate array of floats to hidden_delta\n"); + exit (EXIT_FAILURE); + } + + output_delta = /*alloc_1d_dbl(n_out + 1);*/ (float *)malloc( + ((n_out + 1) * sizeof(float))); + if (output_delta == NULL) { + fprintf(stderr, "Couldn't allocate array of floats to output_delta\n"); + exit (EXIT_FAILURE); + } + + target = /*alloc_1d_dbl(n_out + 1);*/ (float *)malloc( + ((n_out + 1) * sizeof(float))); + if (target == NULL) { + fprintf(stderr, "Couldn't allocate array of floats to target\n"); + exit (EXIT_FAILURE); + } +} + + + + +void bpnn_randomize_weights(float *w, int m, int n) +{ + int i, j; + for (i = 0; i <= m; i++) { + for (j = 0; j <= n; j++) { + w[i * n + i + j] = (float)rand() / RAND_MAX; + } + } +} + +void bpnn_randomize_row(float *w, int m) { + int i; + for (i = 0; i <= m; i++) { + w[i] = 0.1; + } +} + +void bpnn_zero_weights(float *w, int m, int n) { + int i, j; + for (i = 0; i <= m; i++) { + for (j = 0; j <= n; j++) { + w[i * n + i + j] = 0.0; + } + } +} + + + + +int main() { + + + + // Seed for random number generator + srand(SEED); + + int i; + bpnn_create(LAYERSIZE, HIDDEN_SIZE, OUTPUT_SIZE); + + int k; + k = 1; + for (i = 0; i < LAYERSIZE; i++) { + input_units[k] = (float)rand() / RAND_MAX; + k++; + } + + + // entering the training kernel, only one iteration + bpnn_train_kernel(input_n, + hidden_n, + output_n, + input_units, + hidden_units, + output_units, + hidden_delta, + output_delta, + target, + input_weights, + hidden_weights, + input_prev_weights, + hidden_prev_weights); + + bpnn_dump(); + + bpnn_free(); +}