Lecture 9: GPU Memory Spaces

Lecture Summary

  • GPU computing: generalities

  • GPU computing: execution configuration

  • GPU computing: scheduling execution

Prerequisite: Parallelism

  • Coarse grain parallelism: Good for CPUs

    • Few tasks

    • Tasks are heterogeneous

    • Tasks are in general complex, lots of control flow

    • Example: {Bake a cake, make coffee, watch lectures} at the same time

  • Fine grain parallelism: Very good for GPUs, ok for CPUs

    • A lot, a lot of tasks

    • Tasks are basically identical

    • Tasks are in general pretty straightforward, lots of math, not much control flow

    • Example: Image processing (lots of pixels to deal with)

GPU Computing

  • GPGPU: General Purpose GPU Computing

    • Started in the early 2000s using graphics libraries

    • GPUs had high bandwidths

    • Data need to be moved into the GPU to process it (this may be a bottleneck!)

      • PCIe: 16-32 GB/s

      • NVLink: 5-12 times faster than PCIe 3

      • The tradeoff is worth it if the data transfer overhead is smaller than our gain

    • Idea: Use the GPU as a co-processor to handle big, parallel jobs

      • In the meanwhile, the CPU handles control of execution & corner tasks

  • CUDA: Compute Unified Device Architecture, distributed by NVIDIA

    • Eliminated the graphics-constraints associated with GPGPU

    • Enables a general-purpose programming model

  • GPUs:

    • Is a co-processor to the CPU/host

    • Has its own memory (device memory)

    • Runs many threads in parallel

    • The data parallel portion of an application runs on the devices as kernels executed in parallel by many threads

    • As compared to CPU threads:

      • GPUs threads are extremely lightweight

      • A GPU needs 1000s of threads for full efficiency

  • Compute capability vs. CUDA version:

    • Compute capability: Refers to hardware

    • CUDA version: Refers to software that manages the hardware

  • Compatibility issues

    • The CUDA driver API is backward, but not forward compatible

      • Code that works for CUDA 8.0 should work for 11.0, but not the other way around

  • CUDA host stream

    • The CUDA runtime places all calls that invoke the GPU in a stream (i.e., ordered collection) of calls

      • The stream is FIFO: In the picture above, Kernel1 is only called after Kernel0 finishes

    • Asynchronicity between host and device: The host continues execution right after launching a kernel

      • Synchronization can be forced

  • Three opportunities for asynchronous:

    • The GPU and CPU work in async mode

    • The GPU has three engines that can work at the same time (copy-in, copy-out, execution)

    • Multiple GPUs can work at the same time on one host

  • Language supported by CUDA

CUDA: First Example

#include<cuda.h>
#include<iostream>

__global__voidsimpleKernel(int* data)
{
    //this adds a value to a variable stored in global memory
    data[threadIdx.x] += 2*(blockIdx.x+ threadIdx.x);
}

int main()
{
    const int numElems= 4;
    int hostArray[numElems], *devArray;
    
    //allocate memory on the device (GPU); zero out all entries in this device array 
    cudaMalloc((void**)&devArray, sizeof(int) * numElems);
    cudaMemset(devArray, 0, numElems* sizeof(int));
    
    //invoke GPU kernel, with one block that has four threads
    simpleKernel<<<1,numElems>>>(devArray);
    
    //bring the result back from the GPU into the hostArray
    cudaMemcpy(&hostArray, devArray, sizeof(int) * numElems, cudaMemcpyDeviceToHost);
    
    //print out the result to confirm that things are looking good 
    std::cout << "Values stored in hostArray: " << std::endl;
    for (int i = 0; i < numElems; i++)
        std::cout<< hostArray[i] << std::endl;
    
    //release the memory allocated on the GPU 
    cudaFree(devArray);
    return 0;
}

GPU Execution Configuration

  • Nomenclature

    • Host: The CPU executing the "master" thread

    • Device: GPU card, connected to the host through a PCIe connection

    • The host instructs the device to execute kernels

    • Defining the execution configuration: The process in which the host tells the device how many threads should each execute kernels

__global__ void kernelFoo(...); // declaration

dim3 DimGrid(100, 50);        // 2D grid structure, w/ total of 5000 thread blocks 
dim3 DimBlock(4, 8, 8);       // 3D block structure, with 256 threads per block 

kernelFoo<<<DimGrid, DimBlock>>>(...arg list...);

  • The concept of "block" is important since it represents the entity that gets executed by an SM (stream multiprocessor)

  • Threads in each block:

    • The threads can be organized as a 3D structure (x, y, z)

    • Max x- or y- dimension of a block is 1024

    • Max z- dimension of a block is 64

    • Max # threads per block is 1024

  • Threads and blocks have indices

  • 3D layout:

    • Most of the time people use 1D

    • This simplifies memory addressing when processing multi-dimensional data

      • Handling matrices

      • Solving PDEs on 3D subdomains

Example: Matrix Multiplication

  • Scope:

    • Only global memory (no shared memory)

    • Matrix will have a small dimension (one block of threads only)

    • Focus on threadIdx usage & memory transfer between host and device

Code

Note that the following kernel is launched using MatrixMulKernel<<<dimGrid, dimBlock>>>(Md, Nd, Pd) where dimGrid is (1,1,1) and dimBlock is (WIDTH, WIDTH).

  • Words of wisdom: In GPU computing, we use as many threads as data items (tasks, jobs) we have to perform (Number of threads == Number of data items)

  • Understanding what thread does what job is a very common source of error in GPU computing

Typically, in each kernel, we do ...

__global__ void multiply_ab(int* a, int* b, int* c, int size)
{
    int whichEntry = threadIdx.x + blockIdx.x * blockDim.x;
    if (whichEntry < size)  // ... this because ...
        c[whichEntry] = a[whichEntry] * b[whichEntry];
}

... because all blocks launched have the same number of threads, and we need to prevent out-of-bounds indexing. Say we have an array of 1493 elements and we launch two blocks of 1024 threads each, some threads will not do work.

That's probably one of the instances, probably many instances, when you regret that you took 759, because this is not fun. -- Prof. Dan Negrut

PreviousLecture 8: GPU Computing Intro. The CUDA Programming Model. CUDA Execution Configuration.NextLecture 10: GPU Scheduling Issues.

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