matrix_multiplication_cudaflow

# Matrix Multiplication (cudaFlow)

Define a Matrix Multiplication Kernel

Define a Matrix Multiplication Kernel

matrix_multiplication_cudaflow_1GPUAcceleratedMatrixMultiplication
Define a cudaFlow for Matrix Multiplication

Define a cudaFlow for Matrix Multiplication

matrix_multiplication_cudaflow_1DefineAcudaFlowForMatrixMultiplication
Benchmarking

Benchmarking

matrix_multiplication_cudaflow_1MatrixMultiplicationcudaFlowBenchmarking

Following up on Matrix Multiplication, this page studies how to accelerate a matrix multiplication workload on a GPU using tf::cudaFlow.

# Define a Matrix Multiplication Kernel

GPU can perform a lot of parallel computations more than CPUs. It is especially useful for data-intensive computing such as matrix multiplication. With GPU, we express the parallel patterns at a fine-grained level. The kernel, written in CUDA, is described as follows:

    //CUDAkerneltoperformmatrixmultiplication
    __global__voidmatmul(int*A,int*B,int*C,intM,intK,intN){
    introw=blockIdx.y*blockDim.y+threadIdx.y;
    intcol=blockIdx.x*blockDim.x+threadIdx.x;
    intsum=0;
    if(col<N&&row<M){
    for(inti=0;i<K;i++){
    sum+=a[row*K+i]*b[i*N+col];
    }
    c[row*N+col]=sum;
    }
    }

Each CUDA thread corresponds to an element of `C` and compute its result. Instead of storing each matrix in a 2D array, we use 1D layout to ease the data transfer between CPU and GPU. In a row-major layout, an element `(x, y)` in the 2D matrix can be addressed at `x * width + y` in the transformed 1D layout.

# Define a cudaFlow for Matrix Multiplication

The next step is to allocate memory for `A`, `B`, and `C` at a GPU. We create three tasks each calling `cudaMalloc` to allocate space for one matrix. Then, we create a cudaFlow to offload matrix multiplication to a GPU. The entire code is described as follows:

    voidmatrix_multiplication(int*A,int*B,int*C,intM,intK,intN){

    tf::Taskflowtaskflow;
    tf::Executorexecutor;

    //allocatethehostandgpustorageforA
    tf::Taskallocate_a=taskflow.emplace([&](){
    cudaMalloc(&da,M*K*sizeof(int));
    }).name("allocate_a");

    //allocatethehostandgpustorageforB
    tf::Taskallocate_b=taskflow.emplace([&](){
    cudaMalloc(&db,K*N*sizeof(int));
    }).name("allocate_b");

    //allocatethehostandgpustorageforC
    tf::Taskallocate_c=taskflow.emplace([&](){
    cudaMalloc(&dc,M*N*sizeof(int));
    }).name("allocate_c");

    //createacudaFlowtasktorunthematrixmultiplication
    tf::TaskcudaFlow=taskflow.emplace([&](){

    tf::cudaFlowcf;

    //copydatatoda,db,anddc
    tf::cudaTaskcopy_da=cf.copy(da,A,M*K).name("H2D_A");
    tf::cudaTaskcopy_db=cf.copy(db,B,K*N).name("H2D_B");
    tf::cudaTaskcopy_hc=cf.copy(C,dc,M*N).name("D2H_C");

    dim3grid((K+16-1)/16,(M+16-1)/16);
    dim3block(16,16);

    tf::cudaTaskkmatmul=cf.kernel(grid,block,0,matmul,da,db,dc,M,K,N)
    .name("matmul");

    kmatmul.succeed(copy_da,copy_db)
    .precede(copy_hc);

    //launchthecudaFlow
    tf::cudaStreamstream;
    cf.run(stream);
    stream.synchronize();

    }).name("cudaFlow");

    //freethegpustorage
    autofree=taskflow.emplace([&](){
    cudaFree(da);
    cudaFree(db);
    cudaFree(dc);
    }).name("free");

    //createdependency
    cudaFlow.succeed(allocate_a,allocate_b,allocate_c)
    .precede(free);

    //dumpthegraphwithoutunfoldingthecudaFlow
    taskflow.dump(std::cout);

    //runthetaskflow
    executor.run(taskflow).wait();

    //dumptheentireexecutiongraphincludingunfoldedcudaFlow
    taskflow.dump(std::cout);
    }

Within the cudaFlow, we create two host-to-device (H2D) tasks that copy data from `A` and `B` to `da` and `db`, one device-to-host (D2H) task that copies the result from `dc` to `C`, and one kernel task that launches `matmul` on the GPU (by default, GPU 0). H2D tasks precede the kernel and the kernel precedes the D2H task. These GPU operations form a GPU task graph managed by a cudaFlow. The first dump of the taskflow gives the following graph:

A cudaFlow encapsulates a GPU task dependency graph similar to a tf::Subflow (see Subflow Tasking). In order to visualize it, we need to execute the graph first and then dump the taskflow.

# Benchmarking

We run three versions of matrix multiplication, sequential CPU, parallel CPUs, and one GPU, on a machine of 12 Intel i7-8700 CPUs at 3.20 GHz and a Nvidia RTX 2080 GPU using various matrix sizes of `A`, `B`, and `C`.

A | B | C | CPU Sequential | CPU Parallel | GPU Parallel
10x10 | 10x10 | 10x10 | 0.142 ms | 0.414 ms | 82 ms
100x100 | 100x100 | 100x100 | 1.641 ms | 0.733 ms | 83 ms
1000x1000 | 1000x1000 | 1000x1000 | 1532 ms | 504 ms | 85 ms
2000x2000 | 2000x2000 | 2000x2000 | 25688 ms | 4387 ms | 133 ms
3000x3000 | 3000x3000 | 3000x3000 | 104838 ms | 16170 ms | 214 ms
4000x4000 | 4000x4000 | 4000x4000 | 250133 ms | 39646 ms | 427 ms

As the matrix size increases, the speed-up of GPU over CPUs becomes prominent. For example, at `4000x4000`, the GPU runtime is 585.8 times faster than the sequential CPU runtime and is 92.8 times faster than the parallel CPU solutions.