added profilling xls and changes for regression

2 files changed, 226 insertions, 0 deletions

diff --git a/cuda-kernels/TensorCoreMatrixCArrangement.xlsx b/cuda-kernels/TensorCoreMatrixCArrangement.xlsx
new file mode 100644
index 0000000..785ecc3
--- /dev/null
+++ b/cuda-kernels/TensorCoreMatrixCArrangement.xlsx
diff --git a/cuda-kernels/tensorcore_type16_32.cu b/cuda-kernels/tensorcore_type16_32.cu
new file mode 100644
index 0000000..7084bf9
--- /dev/null
+++ b/cuda-kernels/tensorcore_type16_32.cu
@@ -0,0 +1,226 @@
+#include <stdio.h>
+#include <curand.h>
+
+// Define some error checking macros.
+#define cudaErrCheck(stat) { cudaErrCheck_((stat), __FILE__, __LINE__); }
+void cudaErrCheck_(cudaError_t stat, const char *file, int line) {
+   if (stat != cudaSuccess) {
+      fprintf(stderr, "CUDA Error: %s %s %d\n", cudaGetErrorString(stat), file, line);
+   }
+}
+
+#define curandErrCheck(stat) { curandErrCheck_((stat), __FILE__, __LINE__); }
+void curandErrCheck_(curandStatus_t stat, const char *file, int line) {
+   if (stat != CURAND_STATUS_SUCCESS) {
+      fprintf(stderr, "cuRand Error: %d %s %d\n", stat, file, line);
+   }
+}
+
+#include <mma.h>
+using namespace nvcuda;
+
+// Must be multiples of 16 for wmma code to work
+#define MATRIX_M (16)
+#define MATRIX_N (16)
+#define MATRIX_K (16)
+
+
+// The only dimensions currently supported by WMMA
+const int WMMA_M = 16;
+const int WMMA_N = 16;
+const int WMMA_K = 16;
+
+__global__ void wmma_example(half *a, half *b, float *c,half *d_fp16, int M, int N, int K) {
+   //unsigned int start_time=0,end_time=0;
+   //start_time=clock();
+
+   // Declare the fragments
+   wmma::fragment<wmma::matrix_a, WMMA_M, WMMA_N, WMMA_K, half, wmma::col_major> a_frag;
+   wmma::fragment<wmma::matrix_b, WMMA_M, WMMA_N, WMMA_K, half, wmma::col_major> b_frag;
+   wmma::fragment<wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, float> c_frag;
+   wmma::fragment<wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, half> d_frag;
+
+   // Bounds checking
+   wmma::load_matrix_sync(a_frag, a, K);
+   wmma::load_matrix_sync(b_frag, b, K);
+   wmma::load_matrix_sync(c_frag, c, N,wmma::mem_col_major);
+   
+//   for(int i=0; i < c_frag.num_elements; i++) {
+////  			c_frag.x[i]=c_frag.x[i]+c_frag.x[i];
+//     	float temp=c_frag.x[i];
+//	printf("THREAD%d:%d: %f \n",threadIdx.x,i,temp );
+//   }
+   wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
+
+   for(int i=0; i < c_frag.num_elements; i++) {
+ 	d_frag.x[i]=c_frag.x[i];
+   }
+   wmma::store_matrix_sync(d_fp16, d_frag, N, wmma::mem_col_major);
+   //printf("clock=%d",end_time-start_time);
+}
+
+__global__ void convertFp32ToFp16 (half *out, float *in, int n) {
+   int idx = blockDim.x * blockIdx.x + threadIdx.x;
+   if (idx < n) {
+      out[idx] = in[idx];
+   }
+}
+__global__ void convertFp16ToFp32 (float *out, half *in, int n) {
+   int idx = blockDim.x * blockIdx.x + threadIdx.x;
+   if (idx < n) {
+      out[idx] = in[idx];
+   }
+}
+
+int main(int argc, char* argv[]) {
+   float *a_fp32;
+   float *b_fp32;
+   float *c_fp32;
+   float *d_fp32;
+
+   half *a_fp16;
+   half *b_fp16;
+   half *c_fp16;
+   half *d_fp16;
+   
+   float *a_host_wmma;
+   float *b_host_wmma;
+   float *c_host_wmma;
+   float *d_host_wmma;
+   float *d_cal_host_wmma;
+
+   cudaEvent_t startWMMA;
+   cudaEvent_t stopWMMA;
+   
+   
+   cudaErrCheck(cudaEventCreate(&startWMMA));
+   cudaErrCheck(cudaEventCreate(&stopWMMA));
+   
+   // Use tensor cores
+   cudaErrCheck(cudaMalloc((void**)&a_fp32, MATRIX_M * MATRIX_K * sizeof(float)));
+   cudaErrCheck(cudaMalloc((void**)&b_fp32, MATRIX_K * MATRIX_N * sizeof(float)));
+   cudaErrCheck(cudaMalloc((void**)&c_fp32, MATRIX_K * MATRIX_N * sizeof(float)));
+   cudaErrCheck(cudaMalloc((void**)&d_fp32, MATRIX_K * MATRIX_N * sizeof(float)));
+   cudaErrCheck(cudaMalloc((void**)&a_fp16, MATRIX_M * MATRIX_K * sizeof(half)));
+   cudaErrCheck(cudaMalloc((void**)&b_fp16, MATRIX_K * MATRIX_N * sizeof(half)));
+   cudaErrCheck(cudaMalloc((void**)&c_fp16, MATRIX_K * MATRIX_N * sizeof(half)));
+   cudaErrCheck(cudaMalloc((void**)&d_fp16, MATRIX_K * MATRIX_N * sizeof(half)));
+
+
+   a_host_wmma      = (float*)malloc(MATRIX_M * MATRIX_K * sizeof(float));
+   b_host_wmma      = (float*)malloc(MATRIX_K * MATRIX_N * sizeof(float));
+   c_host_wmma      = (float*)malloc(MATRIX_M * MATRIX_N * sizeof(float));
+   d_host_wmma      = (float*)malloc(MATRIX_M * MATRIX_N * sizeof(float));
+   d_cal_host_wmma      = (float*)malloc(MATRIX_M * MATRIX_N * sizeof(float));
+
+   //printf("a_fp32\n");
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_K;n++){
+		a_host_wmma[m*MATRIX_K+n]=(m*MATRIX_K+n)%10;
+	//	printf("%f ",a_host_wmma[m*MATRIX_K+n]);
+	}
+	//printf(";\n");
+   }
+  
+   //printf("b_fp32\n");
+   for(int m=0;m<MATRIX_K;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		b_host_wmma[m*MATRIX_N+n]=(m*MATRIX_N+n)%10;
+	//	printf("%f ",b_host_wmma[m*MATRIX_N+n]);
+	}
+	//	printf(";\n");
+   }
+   
+   //printf("c_fp32\n");
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		c_host_wmma[m*MATRIX_N+n]=(m*MATRIX_N+n);
+		d_cal_host_wmma[m*MATRIX_N+n]=0;
+	//	printf("%f ",c_host_wmma[m*MATRIX_N+n]);
+	}
+   }
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		for(int k=0;k<MATRIX_K;k++){
+			d_cal_host_wmma[m*MATRIX_N+n]+=	a_host_wmma[m*MATRIX_K+k]*b_host_wmma[k*MATRIX_K+n];
+		}
+		d_cal_host_wmma[m*MATRIX_N+n]+=c_host_wmma[m*MATRIX_N+n];
+	}
+   }
+
+
+   cudaErrCheck(cudaMemcpy(a_fp32,a_host_wmma,  MATRIX_M * MATRIX_K * sizeof(float), cudaMemcpyHostToDevice));
+   cudaErrCheck(cudaMemcpy(b_fp32,b_host_wmma,  MATRIX_K * MATRIX_N * sizeof(float), cudaMemcpyHostToDevice));
+   cudaErrCheck(cudaMemcpy(c_fp32,c_host_wmma,  MATRIX_M * MATRIX_N * sizeof(float), cudaMemcpyHostToDevice));
+
+   convertFp32ToFp16 <<< (MATRIX_M * MATRIX_K + 255) / 256, 256 >>> (a_fp16, a_fp32, MATRIX_M * MATRIX_K);
+   convertFp32ToFp16 <<< (MATRIX_K * MATRIX_N + 255) / 256, 256 >>> (b_fp16, b_fp32, MATRIX_K * MATRIX_N);
+
+   printf("\nM = %d, N = %d, K = %d. \n", MATRIX_M, MATRIX_N, MATRIX_K);
+   
+   printf("Running with wmma...\n");
+   cudaErrCheck(cudaEventRecord(startWMMA));
+   wmma_example <<< 1, 32>>> (a_fp16, b_fp16, c_fp32, d_fp16 , MATRIX_M, MATRIX_N, MATRIX_K);
+   cudaErrCheck(cudaEventRecord(stopWMMA));
+   cudaErrCheck(cudaEventSynchronize(stopWMMA));
+
+   convertFp16ToFp32 <<< (MATRIX_M * MATRIX_N + 255) / 256, 256 >>> (d_fp32, d_fp16, MATRIX_K * MATRIX_N);
+  // Error checking
+   printf("\nChecking results...\n");
+   cudaErrCheck(cudaMemcpy(d_host_wmma, d_fp32, MATRIX_M * MATRIX_N * sizeof(float), cudaMemcpyDeviceToHost));
+   
+   printf("Results verified: cublas and WMMA agree.\n\n");
+   float wmmaTime;
+   cudaErrCheck(cudaEventElapsedTime(&wmmaTime, startWMMA, stopWMMA));
+   printf("wmma took %fms\n", wmmaTime);
+   
+   cudaErrCheck(cudaEventDestroy(startWMMA));
+   cudaErrCheck(cudaEventDestroy(stopWMMA));
+   int t=600000;
+   while(t-->0);
+
+   printf("D_CALCULATED\n");
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		printf("%.2f,",d_cal_host_wmma[m*MATRIX_N+n]);
+	}
+	printf("\n");
+   }
+   printf("D_WMMA\n");
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+		printf("%.2f,",d_host_wmma[m*MATRIX_N+n]);
+	}
+	printf("\n");
+   }
+   int suc=1;
+   for(int m=0;m<MATRIX_M;m++){
+	for(int n=0;n<MATRIX_N;n++){
+ 		if(abs(d_cal_host_wmma[m*MATRIX_N+n]-d_host_wmma[m*MATRIX_N+n])>1) 
+		{	
+			printf("ERROR:\n");
+			suc=0;
+		}
+	}
+   }
+   if(suc==1)
+	printf("COMPLETED_SUCCESSFULLY\n");
+   
+   cudaErrCheck(cudaFree(a_fp32));
+   cudaErrCheck(cudaFree(b_fp32));
+   cudaErrCheck(cudaFree(c_fp32));
+   cudaErrCheck(cudaFree(d_fp32));
+   cudaErrCheck(cudaFree(a_fp16));
+   cudaErrCheck(cudaFree(b_fp16));
+   cudaErrCheck(cudaFree(c_fp16));
+   cudaErrCheck(cudaFree(d_fp16));
+
+   free(a_host_wmma);
+   free(b_host_wmma);
+   free(c_host_wmma);
+   free(d_host_wmma);
+   cudaErrCheck(cudaDeviceReset());
+   return 0;
+}
+
+