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als_mpi2.cu
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als_mpi2.cu
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* als.cu
*
* Created on: Feb 10, 2015
* Author: Wei Tan (wtan@us.ibm.com)
* Alternating Least Square for Matrix Factorization on CUDA 7.0+
* Code optimized for F = 100, and on cc 3.5, 3.7 platforms. Also tested in cc 5.2
*/
//do not use fp16 by default
//#define CUMF_USE_HALF
//#define SURPASS_NAN
#define USE_CG
//if cojugate gradient solver generates results in FP16
//#define CUMF_TT_FP16
//#define CUMF_XX_FP16
#define CG_ITER 6
//#define CUMF_SAVE_MODEL
#include "als_mpi.h"
#include "device_utilities.h"
#include "cg.h"
#include "host_utilities.h"
#include "mpi_utils.h"
#include <fstream>
#include <assert.h>
#include <cuda_fp16.h>
#include <omp.h>
#include <pthread.h>
#ifdef CUMF_USE_HALF
#define SCAN_BATCH 24
#else
#define SCAN_BATCH 28
#endif
#include <iostream>
using namespace std;
void saveDeviceFloatArrayToFile(string fileName, int size, float* d_array){
float* h_array;
cudacall(cudaMallocHost( (void** ) &h_array, size * sizeof(h_array[0])) );
cudacall(cudaMemcpy(h_array, d_array, size * sizeof(h_array[0]),cudaMemcpyDeviceToHost));
FILE * outfile = fopen(fileName.c_str(), "wb");
fwrite(h_array, sizeof(float), size, outfile);
fclose(outfile);
cudaFreeHost(h_array);
}
int updateX(const int batch_size, const int batch_offset, float * ythetaT, float * tt, float * XT,
cublasHandle_t handle, const int m, const int n, const int f, const int nnz,
float** devPtrTTHost, float **devPtrYthetaTHost){
#ifdef DEBUG
float elapsed;
struct timeval tv0, tv1, tv2;
gettimeofday(&tv0, NULL);
printf("*******Batch LU factorization of tt.\n");
#endif
//pointers needed by batch op
float **devPtrTT = 0;
int *INFO;
for (int k = 0; k < batch_size; k++) {
devPtrTTHost[k] = &tt[k * f * f];
}
cudacall(cudaMalloc((void** ) &devPtrTT, batch_size * sizeof(*devPtrTT)));
cudacall(cudaMemcpy(devPtrTT, devPtrTTHost, batch_size * sizeof(*devPtrTT),cudaMemcpyHostToDevice));
//cudacall( cudaMalloc(&P, f * batch_size * sizeof(int)) );
cudacall( cudaMalloc(&INFO, batch_size * sizeof(int) ));
cublascall(cublasSgetrfBatched(handle, f, devPtrTT, f, NULL, INFO, batch_size));
cudaThreadSynchronize();
#ifdef DEBUG
gettimeofday(&tv1, NULL);
elapsed = (tv1.tv_sec - tv0.tv_sec)
+ (tv1.tv_usec - tv0.tv_usec) / 1000000.0;
printf("\t %f seconds. \n", elapsed);
printf("*******solve: tt * XT = ythetaT use cublas, with LU decomposition.\n");
#endif
float **devPtrYthetaT = 0;
for (int k = 0; k < batch_size; k++) {
devPtrYthetaTHost[k] = &ythetaT[batch_offset * f + k * f];
}
cudacall(cudaMalloc((void** ) &devPtrYthetaT, batch_size * sizeof(*devPtrYthetaT)));
cudacall(cudaMemcpy(devPtrYthetaT, devPtrYthetaTHost, batch_size * sizeof(*devPtrYthetaT), cudaMemcpyHostToDevice));
int * info2 = (int *) malloc(sizeof(int));
cublascall( cublasSgetrsBatched(handle, CUBLAS_OP_N, f, 1,
(const float ** ) devPtrTT, f, NULL, devPtrYthetaT, f, info2, batch_size) );
cudaThreadSynchronize();
cudaError_t cudaStat1 = cudaGetLastError();
if (cudaStat1 != cudaSuccess) {
fprintf(stderr,"Failed to launch cublasSgetrsBatched (error code: %s)!\n", cudaGetErrorString(cudaStat1));
exit(EXIT_FAILURE);
}
cudacall( cudaMemcpy(&XT[batch_offset * f], &ythetaT[batch_offset * f],
batch_size * f * sizeof(float), cudaMemcpyDeviceToDevice) );
#ifdef DEBUG
gettimeofday(&tv2, NULL);
elapsed = (tv2.tv_sec - tv1.tv_sec)
+ (tv2.tv_usec - tv1.tv_usec) / 1000000.0;
printf("\t %f seconds. \n", elapsed);
#endif
cudacall(cudaFree(devPtrTT));
//cudacall(cudaFree(P));
cudacall(cudaFree(INFO));
cudacall(cudaFree(devPtrYthetaT));
return 0;
}
int updateTheta(const int batch_size, const int batch_offset, float * xx,
float * yTXT, float * thetaT,
cublasHandle_t handle,
const int m, const int n, const int f, const int nnz,
float ** devPtrXXHost, float **devPtrYTXTHost ){
#ifdef DEBUG
float elapsed;
struct timeval tv0, tv1, tv2;
gettimeofday(&tv0, NULL);
printf("*******LU factorize xx.\n");
#endif
float **devPtrXX = 0;
for (int k = 0; k < batch_size; k++) {
devPtrXXHost[k] = &xx[k * f * f];
}
cudacall(cudaMalloc((void** ) &devPtrXX, batch_size * sizeof(*devPtrXX)));
cudacall(cudaMemcpy(devPtrXX, devPtrXXHost, batch_size * sizeof(*devPtrXX), cudaMemcpyHostToDevice));
int *INFO;
//cudacall(cudaMalloc(&P, f * batch_size * sizeof(int)));
cudacall(cudaMalloc(&INFO, batch_size * sizeof(int)));
cublascall(cublasSgetrfBatched(handle, f, devPtrXX, f, NULL, INFO, batch_size));
cudaThreadSynchronize();
#ifdef DEBUG
gettimeofday(&tv1, NULL);
elapsed = (tv1.tv_sec - tv0.tv_sec)
+ (tv1.tv_usec - tv0.tv_usec) / 1000000.0;
printf("\t %f seconds. \n", elapsed);
printf("******* solve xx * thetaT = yTXT with CUDA 7.\n");
#endif
float **devPtrYTXT = 0;
for (int k = 0; k < batch_size; k++) {
devPtrYTXTHost[k] = &yTXT[batch_offset * f + k * f];
}
cudacall(cudaMalloc((void** ) &devPtrYTXT, batch_size * sizeof(*devPtrYTXT)));
cudacall(cudaMemcpy(devPtrYTXT, devPtrYTXTHost, batch_size * sizeof(*devPtrYTXT),cudaMemcpyHostToDevice));
int * info2 = (int *) malloc(sizeof(int));
cublascall( cublasSgetrsBatched(handle, CUBLAS_OP_N, f, 1,
(const float ** ) devPtrXX, f, NULL, devPtrYTXT, f, info2, batch_size) );
cudaThreadSynchronize();
cudaError_t cudaStat1 = cudaGetLastError();
if (cudaStat1 != cudaSuccess) {
fprintf(stderr,"Failed to launch cublasSgetrsBatched (error code: %s)!\n", cudaGetErrorString(cudaStat1));
exit(EXIT_FAILURE);
}
cudacall( cudaMemcpy( &thetaT[batch_offset * f], &yTXT[batch_offset * f],
batch_size * f * sizeof(float), cudaMemcpyDeviceToDevice) );
#ifdef DEBUG
gettimeofday(&tv2, NULL);
elapsed = (tv2.tv_sec - tv1.tv_sec)
+ (tv2.tv_usec - tv1.tv_usec) / 1000000.0;
printf("\t %f seconds. \n", elapsed);
#endif
cudaFree(devPtrXX);
cudaFree(INFO);
free(info2);
cudaFree(devPtrYTXT);
return 0;
}
__global__ void RMSE(const float * csrVal, const int* cooRowIndex,
const int* csrColIndex, const float * __restrict__ thetaT, const float * __restrict__ XT, float * error, const int nnz,
const int error_size, const int f) {
int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i < nnz) {
int row = cooRowIndex[i];
int col = csrColIndex[i];
float e = csrVal[i];
//if(i%1000000==0) printf("row: %d, col: %d, csrVal[%d]: %f.\n", row, col, i, e);
for (int k = 0; k < f; k++) {
e -= __ldg(&thetaT[f * col + k]) * __ldg(&XT[f * row + k]);
}
atomicAdd(&error[i%error_size], e*e);
//if(i%1000000==0) printf("error[%d]: %f.\n", i, e);
}
}
__inline__ __device__
float warpRedSum(float val) {
for (int offset = 32 / 2; offset > 0; offset /= 2) {
val += __shfl_down(val, offset);
}
return val;
}
__inline__ __device__
float blockRedSum(float val) {
static __shared__ int shared[32]; // Shared mem for 32 partial sums
int lane = threadIdx.x % 32;
int wid = threadIdx.x / 32;
val = warpRedSum(val); // Each warp performs partial reduction
if (lane==0) {
shared[wid] = val; // Write reduced value to shared memory
}
__syncthreads(); // Wait for all partial reductions
//read from shared memory only if that warp existed
val = (threadIdx.x < blockDim.x / 32) ? shared[lane] : 0;
if (wid == 0) {
val = warpRedSum(val); //Final reduce within first warp
}
return val;
}
__global__ void RMSE_reduction(const float * csrVal, const int* cooRowIndex,
const int* csrColIndex, const float * __restrict__ thetaT, const float * __restrict__ XT, float * error, const int nnz, const int f) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
float e = 0.0;
if (i < nnz) {
int row = cooRowIndex[i];
int col = csrColIndex[i];
e = csrVal[i];
//if(i%1000000==0) printf("row: %d, col: %d, csrVal[%d]: %f.\n", row, col, i, e);
for (int k = 0; k < f; k++) {
e -= __ldg(&thetaT[f * col + k]) * __ldg(&XT[f * row + k]);
}
e = e * e;
}
float sum = blockRedSum(e);
if (threadIdx.x == 0) {
error[blockIdx.x] = sum;
}
}
__global__ void RMSE_reduction2(const int offset, const float * csrVal, const int* cooRowIndex,
const int* csrColIndex, const float * __restrict__ thetaT, const float * __restrict__ XT, float * error,
const int nnz, const int f) {
__shared__ float sdata[256];
int i = blockDim.x * blockIdx.x + threadIdx.x + offset;
float e = 0.0;
if (i < nnz) {
int row = cooRowIndex[i];
int col = csrColIndex[i];
e = csrVal[i];
//if(i%1000000==0) printf("row: %d, col: %d, csrVal[%d]: %f.\n", row, col, i, e);
for (int k = 0; k < f; k++) {
e -= __ldg(&thetaT[f * col + k]) * __ldg(&XT[f * row + k]);
}
e = e * e;
}
sdata[threadIdx.x] = e;
__syncthreads();
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) {
if (threadIdx.x < s) {
sdata[threadIdx.x] += sdata[threadIdx.x + s];
}
__syncthreads();
}
if (threadIdx.x == 0) {
error[blockIdx.x] = sdata[0];
}
}
__global__ void reduction(float *in, float *out, int N) {
float sum = 0.0;
//reduce multiple elements per thread
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; i += blockDim.x * gridDim.x) {
sum += in[i];
}
sum = blockRedSum(sum);
if (threadIdx.x == 0)
out[blockIdx.x] = sum;
}
__global__ void
__launch_bounds__(64)
get_hermitian100(const int batch_offset, float2* tt,
const int* csrRowIndex, const int* csrColIndex, const float lambda, const int m, const int F,
const float2* __restrict__ thetaT) {
extern __shared__ float2 thetaTemp[];
int row = blockIdx.x + batch_offset;
if (row < m) {
//this block needs to handle end - start thetaT columns
int start = csrRowIndex[row];
int end = csrRowIndex[row + 1];
//slide through [start, end] by window size SCAN_BATCH
int iterations = (end - start - 1)/SCAN_BATCH + 1;
float temp0= 0, temp1= 0, temp2= 0, temp3= 0, temp4= 0, temp5= 0, temp6= 0, temp7= 0, temp8= 0, temp9 = 0;
float temp10= 0, temp11= 0, temp12= 0, temp13= 0, temp14= 0, temp15= 0, temp16= 0, temp17= 0, temp18= 0, temp19 = 0;
float temp20= 0, temp21= 0, temp22= 0, temp23= 0, temp24= 0, temp25= 0, temp26= 0, temp27= 0, temp28= 0, temp29 = 0;
float temp30= 0, temp31= 0, temp32= 0, temp33= 0, temp34= 0, temp35= 0, temp36= 0, temp37= 0, temp38= 0, temp39 = 0;
float temp40= 0, temp41= 0, temp42= 0, temp43= 0, temp44= 0, temp45= 0, temp46= 0, temp47= 0, temp48= 0, temp49 = 0;
float temp50= 0, temp51= 0, temp52= 0, temp53= 0, temp54= 0, temp55= 0, temp56= 0, temp57= 0, temp58= 0, temp59 = 0;
float temp60= 0, temp61= 0, temp62= 0, temp63= 0, temp64= 0, temp65= 0, temp66= 0, temp67= 0, temp68= 0, temp69 = 0;
float temp70= 0, temp71= 0, temp72= 0, temp73= 0, temp74= 0, temp75= 0, temp76= 0, temp77= 0, temp78= 0, temp79 = 0;
float temp80= 0, temp81= 0, temp82= 0, temp83= 0, temp84= 0, temp85= 0, temp86= 0, temp87= 0, temp88= 0, temp89 = 0;
float temp90= 0, temp91= 0, temp92= 0, temp93= 0, temp94= 0, temp95= 0, temp96= 0, temp97= 0, temp98= 0, temp99 = 0;
int tile_x = 0;
int tile_y = 0;
int tile = F/10;
for ( int i = 0; i < 10; i++){
int end = ((20-i)*(i+1))/2;
if(threadIdx.x < end){
tile_x = i * tile;
tile_y = (10 + threadIdx.x - end) * tile;
break;
}
}
//iteration: copy gmem-->smem; aggregate smem-->register
for (int iter = 0; iter < iterations; iter ++){
//copy texture --> smem, and sync
/*
This is the fastest implementation
thetaT is NOT coalesced loaded but cached by L1 and L2
faster than coalesced version (see the next paragraph commented out)
because it concurrently load multiple thetaT columns
two threads per theta column, e.g., threads 0 & 1 for theta[0], threads 2 & 3 for theta[1]
require: blockDim.x (64) >= 2*SCAN_BATCH
*/
///*
if(threadIdx.x < 2*SCAN_BATCH){
int anchor = start + iter*SCAN_BATCH + threadIdx.x/2;
if(anchor < end){
int col = csrColIndex[anchor];
//IMPORTANT: for loop has constant and identical start and end
for (int k = 0; k < 50; k += 2)
//thetaTemp[threadIdx.x*F/4 + k/2] =__ldg(&thetaT[ F/2 * col + threadIdx.x%2*F/4 + k/2]);
thetaTemp[threadIdx.x*F/4 + k/2] = thetaT[ F/2 * col + threadIdx.x%2*F/4 + k/2];
}
}
//*/
/*
//coalesced load thetaT, has to load column by column, less concurrency, worse performance
int anchor = start + iter*SCAN_BATCH + threadIdx.x%32;
int col_local;
if(anchor < end && threadIdx.x%32 < SCAN_BATCH)
col_local = csrColIndex[anchor];
int stop = (end - start - iter*SCAN_BATCH < SCAN_BATCH)? end - start - iter*SCAN_BATCH: SCAN_BATCH;
for (int k = 0; k < stop; k++){
//deal with col_local in lane[k]
int col = __shfl(col_local, k);
//if(blockIdx.x==0 && threadIdx.x==0)
// printf("iter=%d,k=%d,col=%d,stop=%d,anchor=%d\n", iter,k, col, stop, anchor);
//this type of for is bad in performance
//for(int i = threadIdx.x; i < F; i += 64)
if(threadIdx.x<F/2)
thetaTemp[k*F/2 + threadIdx.x] = __ldg(&thetaT[ F/2 * col + threadIdx.x]);
}
*/
__syncthreads();
///*
//tile: 10*10
if(threadIdx.x < 55){
if(iter < iterations - 1){
for(int k = 0; k < SCAN_BATCH; k++)
accumulate_in_registers();
}
else{
for(int k = 0; k < end - start - iter*SCAN_BATCH; k++)
accumulate_in_registers();
}
}
//*/
}
//end of iteration in copying from smem and aggregating in register
__syncthreads();
#ifdef DEBUG
//if(threadIdx.x==0)
// printf("***temp 0~9: %f %f %f %f %f %f %f %f %f %f\n", temp0, temp1, temp2, temp3, temp4, temp5, temp6, temp7, temp8, temp9);
#endif
if(threadIdx.x < 55 ){
//weighted-lambda regularization
if(tile_x == tile_y){
float temp = (end - start) * lambda;
temp0 += temp;
temp11 += temp;
temp22 += temp;
temp33 += temp;
temp44 += temp;
temp55 += temp;
temp66 += temp;
temp77 += temp;
temp88 += temp;
temp99 += temp;
}
//copy output to gmem
int index = blockIdx.x*F*F/2;
//fill_lower_half_from_registers();
fill_lower_half_from_registers_float2();
//symmetric
if(tile_x!=tile_y){
//fill_upper_half_from_registers();
fill_upper_half_from_registers_float2();
}
}
}
}
/*a generic kernel to get the hermitian matrices
* as the left-hand side of the equations, to update X in ALS
*examplary F = 100, T = 10
*/
__global__ void
get_hermitianT10(const int batch_offset, float* tt,
const int* csrRowIndex, const int* csrColIndex, const float lambda, const int m, const int F,
const float* __restrict__ thetaT) {
extern __shared__ float2 thetaTemp [];
int row = blockIdx.x + batch_offset;
if (row < m) {
//this block needs to handle end - start thetaT columns
int start = csrRowIndex[row];
int end = csrRowIndex[row + 1];
//slide through [start, end] by window size SCAN_BATCH
int iterations = (end - start - 1)/SCAN_BATCH + 1;
float temp0= 0, temp1= 0, temp2= 0, temp3= 0, temp4= 0, temp5= 0, temp6= 0, temp7= 0, temp8= 0, temp9 = 0;
float temp10= 0, temp11= 0, temp12= 0, temp13= 0, temp14= 0, temp15= 0, temp16= 0, temp17= 0, temp18= 0, temp19 = 0;
float temp20= 0, temp21= 0, temp22= 0, temp23= 0, temp24= 0, temp25= 0, temp26= 0, temp27= 0, temp28= 0, temp29 = 0;
float temp30= 0, temp31= 0, temp32= 0, temp33= 0, temp34= 0, temp35= 0, temp36= 0, temp37= 0, temp38= 0, temp39 = 0;
float temp40= 0, temp41= 0, temp42= 0, temp43= 0, temp44= 0, temp45= 0, temp46= 0, temp47= 0, temp48= 0, temp49 = 0;
float temp50= 0, temp51= 0, temp52= 0, temp53= 0, temp54= 0, temp55= 0, temp56= 0, temp57= 0, temp58= 0, temp59 = 0;
float temp60= 0, temp61= 0, temp62= 0, temp63= 0, temp64= 0, temp65= 0, temp66= 0, temp67= 0, temp68= 0, temp69 = 0;
float temp70= 0, temp71= 0, temp72= 0, temp73= 0, temp74= 0, temp75= 0, temp76= 0, temp77= 0, temp78= 0, temp79 = 0;
float temp80= 0, temp81= 0, temp82= 0, temp83= 0, temp84= 0, temp85= 0, temp86= 0, temp87= 0, temp88= 0, temp89 = 0;
float temp90= 0, temp91= 0, temp92= 0, temp93= 0, temp94= 0, temp95= 0, temp96= 0, temp97= 0, temp98= 0, temp99 = 0;
int N = F/T10; // N = 100/10=10; for F = 100 and T = 10
int effective_block_size = N*(N+1)/2;
//get the x and y coordinate
int tile_x = 0;
int tile_y = 0;
for ( int i = 0; i < N; i++ ) {
int end = ((2*N-i)*(i+1))/2;
if(threadIdx.x < end){
tile_x = i * T10;
tile_y = (N + threadIdx.x - end) * T10;
break;
}
}
int index = blockIdx.x*F*F;
//iteration: copy gmem-->smem; aggregate smem-->register
for (int iter = 0; iter < iterations; iter ++){
//phase 1 in iteration: gmem --> smem
//REQ: blockDim.x >= F/2
if(threadIdx.x < F/2){
for(int k = 0; k< SCAN_BATCH; k++){
if(iter*SCAN_BATCH + k < end - start){
float2 theta;
theta.x = __ldg(&thetaT[F * csrColIndex[start + iter*SCAN_BATCH + k] + 2*threadIdx.x]);
theta.y = __ldg(&thetaT[F * csrColIndex[start + iter*SCAN_BATCH + k] + 2*threadIdx.x+1]);
thetaTemp[k * F/2 + threadIdx.x] = theta;
//this simpler statement is slower.
//thetaTemp[k * F/2 + threadIdx.x] = __ldg((float2*)&thetaT[F * csrColIndex[start + iter*SCAN_BATCH + k] + 2*threadIdx.x]);
}
//not enough theta to copy, set zero
else
memset(&thetaTemp[k*F/2 + threadIdx.x], 0, 2*sizeof(float));
}
}
__syncthreads();
//phase 2 in iteration: smem --> register
if(threadIdx.x < effective_block_size){//this redundant "if" seems improving kernel performance
for(int k = 0; k < SCAN_BATCH; k++){
accumulate_in_registers();
}
}
}
//end of iteration in copying from smem and aggregating in register
__syncthreads();
//phase 3, after iteration: register --> gmem
if(threadIdx.x < effective_block_size){
fill_lower_half_from_registers();
//symmetric
if(tile_x != tile_y){
fill_upper_half_from_registers();
}
//regularization
if(tile_x == tile_y){
for(int k = 0; k < T10; k++)
tt[index + (tile_x+k)*(1+F)] += (end - start) * lambda;
}
}
}
}
typedef struct {
int device;
int mpi_rank;
int n_procs;
int batch_offset;
int batch_size;
int f;
cudaStream_t stream;
float *buff;
} comm_arg_t;
void *mpi_communicate_thread(void *arg) {
comm_arg_t *comm_arg = (comm_arg_t *) arg;
cudacall(cudaSetDevice(comm_arg->device));
size_t sz = comm_arg->batch_size * comm_arg->f;
float *tmp = (float *) malloc(sz * sizeof(float));
cudacall(cudaMemcpyAsync(tmp, comm_arg->buff, sz * sizeof(float), cudaMemcpyDeviceToHost, comm_arg->stream));
cudacall(cudaStreamSynchronize(comm_arg->stream));
void *req[comm_arg->n_procs - 1];
int cnt = 0;
for (int i = 0; i < comm_arg->n_procs; ++i) {
if (i != comm_arg->mpi_rank) {
//printf("Node %d send to node %d at offset %d with size %d\n", comm_arg->mpi_rank, i, comm_arg->batch_offset, sz);
req[cnt++] = MPI_Isend_float_wrapper(tmp, sz, i, comm_arg->batch_offset * comm_arg->f);
}
}
/*MPI_Waitall_wrapper(req, cnt);
printf("MPI send buffer on node %d is freed\n", comm_arg->mpi_rank);
free(tmp);*/
free(arg);
return NULL;
}
typedef struct {
int f;
int m;
int nDevices;
int device_base;
int X_BATCH;
int n_procs;
int mpi_rank;
float **XT;
float *XT_buff;
} rev_x_arg_t;
void *mpi_rev_x_thread(void *arg) {
rev_x_arg_t *rev_arg = (rev_x_arg_t *) arg;
int device_base = rev_arg->device_base;
int nDevices = rev_arg->nDevices;
int X_BATCH = rev_arg->X_BATCH;
int n_procs = rev_arg->n_procs;
int mpi_rank = rev_arg->mpi_rank;
int f = rev_arg->f;
int m = rev_arg->m;
float **XT = rev_arg->XT;
float *XT_buff = rev_arg->XT_buff;
cudaStream_t stream[nDevices];
for (int device = 0; device < nDevices; ++device) {
cudacall(cudaSetDevice(device_base + device));
cudacall(cudaStreamCreate(&stream[device]));
}
int bound = ((X_BATCH - 1) / n_procs + 1) * (mpi_rank + 1);
if (bound > X_BATCH) {
bound = X_BATCH;
}
for (int batch_id = 0; batch_id < X_BATCH; ++batch_id) {
if (batch_id >= ((X_BATCH - 1) / n_procs + 1) * mpi_rank && batch_id < bound) {
continue;
}
int batch_size = 0;
if (batch_id != X_BATCH - 1) {
batch_size = m / X_BATCH;
} else {
batch_size = m - batch_id * (m / X_BATCH);
}
int batch_offset = batch_id * (m / X_BATCH);
int src = batch_id / ((X_BATCH - 1) / n_procs + 1);
//printf("Node %d receives from node %d at offset %d with size %d\n", mpi_rank, src, batch_offset, batch_size * f);
MPI_Recv_float_wrapper(&XT_buff[batch_offset * f], batch_size * f, src, batch_offset * f);
for (int device = 0; device < nDevices; ++device) {
cudacall(cudaSetDevice(device_base + device));
cudacall(cudaMemcpyAsync(&XT[device][batch_offset * f], &XT_buff[batch_offset * f], batch_size * f * sizeof(float), cudaMemcpyHostToDevice, stream[device]));
}
}
free(arg);
return NULL;
}
typedef struct {
int f;
int n;
int nDevices;
int device_base;
int THETA_BATCH;
int n_procs;
int mpi_rank;
float **thetaT;
float *thetaT_buff;
} rev_theta_arg_t;
void *mpi_rev_theta_thread(void *arg) {
rev_theta_arg_t *rev_arg = (rev_theta_arg_t *) arg;
int device_base = rev_arg->device_base;
int nDevices = rev_arg->nDevices;
int THETA_BATCH = rev_arg->THETA_BATCH;
int n_procs = rev_arg->n_procs;
int mpi_rank = rev_arg->mpi_rank;
int f = rev_arg->f;
int n = rev_arg->n;
float **thetaT = rev_arg->thetaT;
float *thetaT_buff = rev_arg->thetaT_buff;
cudaStream_t stream[nDevices];
for (int device = 0; device < nDevices; ++device) {
cudacall(cudaSetDevice(device_base + device));
cudacall(cudaStreamCreate(&stream[device]));
}
int bound = ((THETA_BATCH - 1) / n_procs + 1) * (mpi_rank + 1);
if (bound > THETA_BATCH) {
bound = THETA_BATCH;
}
for (int batch_id = 0; batch_id < THETA_BATCH; ++batch_id) {
if (batch_id >= ((THETA_BATCH - 1) / n_procs + 1) * mpi_rank && batch_id < bound) {
continue;
}
int batch_size = 0;
if(batch_id != THETA_BATCH - 1) {
batch_size = n / THETA_BATCH;
} else {
batch_size = n - batch_id * (n / THETA_BATCH);
}
int batch_offset = batch_id * (n / THETA_BATCH);
int src = batch_id / ((THETA_BATCH - 1) / n_procs + 1);
//printf("Node %d receives from node %d at offset %d with size %d\n", mpi_rank, src, batch_offset, batch_size * f);
MPI_Recv_float_wrapper(&thetaT_buff[batch_offset * f], batch_size * f, src, batch_offset * f);
for (int device = 0; device < nDevices; ++device) {
cudacall(cudaSetDevice(device_base + device));
cudacall(cudaMemcpyAsync(&thetaT[device][batch_offset * f], &thetaT_buff[batch_offset * f], batch_size * f * sizeof(float), cudaMemcpyHostToDevice, stream[device]));
}
}
free(arg);
return NULL;
}
float doALS(const int* csrRowIndexHostPtr, const int* csrColIndexHostPtr, const float* csrValHostPtr,
const int* cscRowIndexHostPtr, const int* cscColIndexHostPtr, const float* cscValHostPtr,
const int* cooRowIndexHostPtr, float* thetaTHost, float* XTHost,
const int * cooRowIndexTestHostPtr, const int * cooColIndexTestHostPtr, const float * cooValHostTestPtr,
const int m, const int n, const int f, const long nnz, const long nnz_test, const float lambda,
const int ITERS, const int X_BATCH, const int THETA_BATCH, int mpi_rank, int n_procs)
{
int nDevices;
cudacall(cudaGetDeviceCount(&nDevices));
assert(nDevices % n_procs == 0);
nDevices = nDevices / n_procs;
int device_base = mpi_rank * nDevices;
omp_set_num_threads(nDevices);
cudaStream_t stream[nDevices + 2][nDevices];
for (int device = 0; device < nDevices; ++device) {
cudacall(cudaSetDevice(device_base + device));
for (int i = 0; i < nDevices + 2; ++i) {
cudacall(cudaStreamCreate(&stream[i][device]));
}
}
printf("*******parameters: m: %d, n: %d, f: %d, nnz: %ld \n", m, n, f, nnz);
//device pointers
float * csrVal[nDevices];
float * thetaT[nDevices];
float * XT[nDevices];
float * cscVal[nDevices];
int * cscRowIndex[nDevices];
int * cscColIndex[nDevices];
int * csrRowIndex[nDevices];
int * csrColIndex[nDevices];
//coo to calculate RMSE
int * cooRowIndex[nDevices];
float * cooVal_test[nDevices];
int * cooRowIndex_test[nDevices];
int * cooColIndex_test[nDevices];
float * errors_train[nDevices];
float * errors_test[nDevices];
float final_rmse = 0;
int nnz_node = (nnz - 1) / n_procs + 1;
int nnz_test_node = (nnz_test - 1) / n_procs + 1;
int nnz_device = (nnz_node - 1) / nDevices + 1;
int nnz_test_device = (nnz_test_node - 1) / nDevices + 1;
int error_size_train = (nnz_device - 1) / 256 + 1;
int error_size_test = (nnz_test_device - 1) / 256 + 1;
float *rmse_train_device[nDevices];
float *rmse_test_device[nDevices];
printf("*******start allocating memory on GPU...\n");
printf("*******start copying memory to GPU...\n");
for (int device = 0; device < nDevices; ++device) {
cudacall(cudaSetDevice(device_base + device));
//dimension: M*F
cudacall(cudaMalloc((void** ) &XT[device], f * m * sizeof(XT[0][0])));
//dimension: F*N
cudacall(cudaMalloc((void** ) &thetaT[device], f * n * sizeof(thetaT[0][0])));
cudacall(cudaMalloc((void** ) &cscRowIndex[device], nnz * sizeof(cscRowIndex[0][0])));
cudacall(cudaMalloc((void** ) &cscColIndex[device], (n+1) * sizeof(cscColIndex[0][0])));
cudacall(cudaMalloc((void** ) &csrRowIndex[device],(m + 1) * sizeof(csrRowIndex[0][0])));
cudacall(cudaMalloc((void** ) &csrColIndex[device], nnz * sizeof(csrColIndex[0][0])));
cudacall(cudaMemcpy(thetaT[device], thetaTHost, (size_t ) (n * f * sizeof(thetaT[0][0])), cudaMemcpyHostToDevice));
//CG needs XT
cudacall(cudaMemcpy(XT[device], XTHost, (size_t ) (m * f * sizeof(XT[0][0])), cudaMemcpyHostToDevice));
cudacall(cudaMemcpy(cscRowIndex[device], cscRowIndexHostPtr, (size_t ) nnz * sizeof(cscRowIndex[0][0]), cudaMemcpyHostToDevice));
cudacall(cudaMemcpy(cscColIndex[device], cscColIndexHostPtr, (size_t ) (n+1) * sizeof(cscColIndex[0][0]), cudaMemcpyHostToDevice));
cudacall(cudaMemcpy(csrRowIndex[device], csrRowIndexHostPtr,(size_t ) ((m + 1) * sizeof(csrRowIndex[0][0])), cudaMemcpyHostToDevice));
cudacall(cudaMemcpy(csrColIndex[device], csrColIndexHostPtr,(size_t ) (nnz * sizeof(csrColIndex[0][0])), cudaMemcpyHostToDevice));
cudacall(cudaMalloc((void** ) &cooRowIndex[device], nnz * sizeof(cooRowIndex[0][0])));
cudacall(cudaMemcpy(cooRowIndex[device], cooRowIndexHostPtr,(size_t ) (nnz * sizeof(cooRowIndex[0][0])), cudaMemcpyHostToDevice));
cudacall(cudaMalloc((void** ) &csrVal[device], nnz * sizeof(csrVal[0][0])));
cudacall(cudaMemcpy(csrVal[device], csrValHostPtr, (size_t ) (nnz * sizeof(csrVal[0][0])), cudaMemcpyHostToDevice));
cudacall(cudaMalloc((void** ) &cooRowIndex_test[device], nnz_test * sizeof(cooRowIndex_test[0][0])));
cudacall(cudaMemcpy(cooRowIndex_test[device], cooRowIndexTestHostPtr,(size_t ) (nnz_test * sizeof(cooRowIndex_test[0][0])), cudaMemcpyHostToDevice));
cudacall(cudaMalloc((void** ) &cooColIndex_test[device], nnz_test * sizeof(cooColIndex_test[0][0])));
cudacall(cudaMalloc((void** ) &cooVal_test[device], nnz_test * sizeof(cooVal_test[0][0])));
cudacall(cudaMemcpy(cooColIndex_test[device], cooColIndexTestHostPtr,(size_t ) (nnz_test * sizeof(cooColIndex_test[0][0])), cudaMemcpyHostToDevice));
cudacall(cudaMemcpy(cooVal_test[device], cooValHostTestPtr,(size_t ) (nnz_test * sizeof(cooVal_test[0][0])),cudaMemcpyHostToDevice));
cudacall(cudaMalloc((void** ) &errors_train[device], error_size_train * sizeof(errors_train[0][0])));
cudacall(cudaMalloc((void** ) &errors_test[device], error_size_test * sizeof(errors_test[0][0])));
cudacall(cudaMalloc((void** ) &rmse_train_device[device], sizeof(rmse_train_device[0][0])));
cudacall(cudaMalloc((void** ) &rmse_test_device[device], sizeof(rmse_test_device[0][0])));
cudacall(cudaMalloc((void** ) &cscVal[device], nnz * sizeof(cscVal[0][0])));
cudacall(cudaMemcpy(cscVal[device], cscValHostPtr,(size_t ) (nnz * sizeof(cscVal[0][0])),cudaMemcpyHostToDevice));
cudacall(cudaDeviceSetCacheConfig(cudaFuncCachePreferShared));
//64-bit smem access
//http://acceleware.com/blog/maximizing-shared-memory-bandwidth-nvidia-kepler-gpus
cudaDeviceSetSharedMemConfig(cudaSharedMemBankSizeEightByte);
}
//initialize cublas, cusparse
cublasHandle_t handle[nDevices];
cusparseHandle_t cushandle[nDevices];
cusparseMatDescr_t descr[nDevices];
#pragma omp parallel
{
int device = omp_get_thread_num();
cudacall(cudaSetDevice(device_base + device));
cublascall(cublasCreate(&handle[device]));
cusparsecall(cusparseCreate(&cushandle[device]));
cusparsecall( cusparseCreateMatDescr(&descr[device]));
cusparseSetMatType(descr[device], CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descr[device], CUSPARSE_INDEX_BASE_ZERO);
}
using namespace std;
#ifdef DEBUG
//variable used to time
double t0 = 0;
double t1 = 0;
#endif
double t_itr = seconds();
printf("*******start iterations...\n");
for(int iter = 0; iter < ITERS ; iter ++){
#ifdef DEBUG
printf("---------------------------ALS iteration %d, update X.----------------------------------\n", iter);
t0 = seconds();
t1 = seconds();
#endif
float * tt[nDevices];
float * ythetaT[nDevices];
float * ytheta[nDevices];
#ifdef DEBUG
printf("\tgenerate: Y*theta using cusparse.\n");
#endif
for (int device = 0; device < nDevices; ++device) {
cudacall(cudaSetDevice(device_base + device));
cudacall(cudaMalloc((void** ) &ythetaT[device], f * m * sizeof(ythetaT[0][0])));
cudacall(cudaMalloc((void** ) &ytheta[device], f * m * sizeof(ytheta[0][0])));
}
#pragma omp parallel
{
int device = omp_get_thread_num();
cudacall(cudaSetDevice(device_base + device));
const float alpha = 1.0f;
const float beta = 0.0f;
cusparsecall(cusparseScsrmm2(cushandle[device], CUSPARSE_OPERATION_NON_TRANSPOSE,
CUSPARSE_OPERATION_TRANSPOSE, m, f, n, nnz, &alpha, descr[device], csrVal[device],
csrRowIndex[device], csrColIndex[device], thetaT[device], f, &beta, ytheta[device], m) );
cublascall(cublasSgeam(handle[device], CUBLAS_OP_T, CUBLAS_OP_N, f, m, &alpha,
(const float * ) ytheta[device], m, &beta, ythetaT[device], f, ythetaT[device], f));
cudacall(cudaDeviceSynchronize());
cudacall(cudaFree(ytheta[device]));
}
#ifdef DEBUG
printf("\tgenerate: Y*theta run %f seconds.\n", seconds() - t1);
#endif
int block_dim = f/T10*(f/T10+1)/2;
if (block_dim < f/2) block_dim = f/2;
int bound = ((X_BATCH - 1) / n_procs + 1) * (mpi_rank + 1);
if (bound > X_BATCH) {
bound = X_BATCH;
}
float *XT_buff = (float *) malloc(f * m * sizeof(float));
pthread_t x_thread;
rev_x_arg_t *rev_arg = (rev_x_arg_t *) malloc(sizeof(rev_x_arg_t));
rev_arg->f = f;
rev_arg->m = m;
rev_arg->nDevices = nDevices;
rev_arg->device_base = device_base;
rev_arg->X_BATCH = X_BATCH;
rev_arg->n_procs = n_procs;
rev_arg->mpi_rank = mpi_rank;
rev_arg->XT = XT;
rev_arg->XT_buff = XT_buff;
assert(pthread_create(&x_thread, NULL, mpi_rev_x_thread, (void *) rev_arg) == 0);
for(int batch_id_base = ((X_BATCH - 1) / n_procs + 1) * mpi_rank; batch_id_base < bound; batch_id_base += nDevices){
#pragma omp parallel
{
int device = omp_get_thread_num();
int batch_id = batch_id_base + device;
if (batch_id < bound) {
cudacall(cudaSetDevice(device_base + device));
#ifdef DEBUG
printf("*******batch %d / %d on node %d device %d*******\n", batch_id, X_BATCH, mpi_rank, device);
#endif
int batch_size = 0;
if(batch_id != X_BATCH - 1)
batch_size = m/X_BATCH;
else
batch_size = m - batch_id*(m/X_BATCH);
int batch_offset = batch_id * (m/X_BATCH);
cudacall(cudaMalloc((void** ) &tt[device], batch_size * f * f * sizeof(float)));
if(f == 100){
get_hermitian100<<<batch_size, 64, SCAN_BATCH * f/2 * sizeof(float2), stream[0][device]>>>
(batch_offset, (float2*)tt[device], csrRowIndex[device], csrColIndex[device], lambda, m, f, (float2*)thetaT[device]);
//This commented out is the fused kernel
//performance not good due to register pressure and low occupancy
//alsUpdateFeature100Host
// (batch_offset, csrRowIndex, csrColIndex, lambda, m, f, thetaT, XT, ythetaT, 6);
}
else {
get_hermitianT10<<<batch_size, block_dim, SCAN_BATCH * f/2 * sizeof(float2), stream[0][device]>>>
(batch_offset, tt[device], csrRowIndex[device], csrColIndex[device], lambda, m, f, thetaT[device]);
}
updateXWithCGHostAsync(tt[device], &XT[device][batch_offset*f], &ythetaT[device][batch_offset*f], batch_size, f, CG_ITER, &stream[0][device]);
}
}
#pragma omp parallel
{
int device = omp_get_thread_num();
int batch_id = batch_id_base + device;
if (batch_id < bound) {
pthread_t t;
comm_arg_t *comm_arg = (comm_arg_t *) malloc(sizeof(comm_arg_t));
cudacall(cudaSetDevice(device_base + device));
cudacall(cudaStreamSynchronize(stream[0][device]));
int batch_size = 0;
if(batch_id != X_BATCH - 1)
batch_size = m/X_BATCH;
else
batch_size = m - batch_id*(m/X_BATCH);
int batch_offset = batch_id * (m/X_BATCH);
comm_arg->device = device_base + device;
comm_arg->mpi_rank = mpi_rank;
comm_arg->n_procs = n_procs;
comm_arg->batch_offset = batch_offset;
comm_arg->batch_size = batch_size;