portability with opencl 1. high performance landscape high performance computing is trending towards...

Portability with OpenCL

1

High Performance Landscape

• High Performance computing is trending towards large number of cores and accelerators

• It would be nice to write your code once and have it be portable across these platforms

• The selection of a programming model can impact your portability

2

Platforms

• Multi-core CPU– An 8-core Sandy Bridge with AVX instructions has 64 GPU

core-equivalents (each core can issue 5 instructions per clock cycle and can store the state of two threads. The standard CPUs will execute at a higher clock rate than a standard GPU

– The Intel Xeon Phi Coprocessor (IXPC) has up to 61 cores which perform 16 single precision operations in a single instruction or 976 GPU cores.

– NVIDIA Kepler SMX has 2880 GPU cores– An AMD Radeon GPU has up to 32 compute units that can

issue 4 instructions per cycle which could be treated as 128 cores

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CPU+Accelerator architecture

4

Memory Management

• CUDA C requires the programmer to allocate device memory and explicitly copy data between host and device.

• OpenCL requires the programmer to allocate buffers and copy data to the buffers. It hides some important details, however, in that it doesn't expose exactly where the buffer lives at various points during the program execution.

• OpenACC allow the programmer to rely entirely on the compiler for memory management to get started, but offer optional data constructs and clauses to control and optimize when data is allocated on the device and moved between host and device

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Parallelism Scheduling

• CUDA and OpenCL have thread, block, grid abstractions

• OpenMP provides core control, but most of the process is automated.

• OpenACC exposes the three levels of parallelism as gang, worker and vector parallelism

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Multithreading

• All three devices require oversubscription to keep the compute units busy; that is, the program must expose extra (slack) parallelism so a compute unit can swap in another active thread when a thread stalls on memory or other long latency operations

• In CUDA and OpenCL, slack parallelism comes from creating blocks or workgroups larger than the number of cores.

• OpenMP allocates multiple iterations per core.

• OpenACC worker-level parallelism is intended to address this issue directly. On the GPUs, iterations of a worker-parallel loop will run on the same core

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Caching and Scratchpad Memories

• In CUDA and OpenCL, the programmer must manage the scratchpad memory explicitly, using CUDA __shared__ or OpenCL __local memory

• OpenACC has a cache directive to allow the programmer to tell the implementation what data has enough reuse to cache locally

8

Portability

• There are three levels of portability.

• First is language portability, meaning a programmer can use the same language to write a program for different targets, even if the programs must be different.

• Second is functional portability, meaning a programmer can write one program that will run on different targets, though not all targets will get the best performance.

• Third is performance portability, meaning a programmer can write one program that gives good performance across many targets.

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Portability• CUDA provides reasonable portability across NVIDIA

devices but there is no pretense that these provide cross-vendor portability, or even performance portability of CUDA source code.

• OpenCL is designed to provide language and functionality portability. Research has demonstrated that even across similar devices, like NVIDIA and AMD GPUs, retuning or rewriting a program can have a significant impact on performance.

• OpenACC is also intended to provide performance portability across devices, and there is some initial evidence to support this claim.

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OpenCL Parallelism Concept

CUDA Equivalent

kernel kernel

host program host program

NDRange (index space) grid

work item thread

work group block

OpenCL to CUDA Data Parallelism Model Mapping

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Overview of OpenCL Execution Model

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OpenCL API Call Explanation CUDA Equivalent

get_global_id(0); global index of the work item in the x dimension

blockIdx.x×blockDim.x+threadIdx.x

get_local_id(0) local index of the work item within the work group in the x dimension

blockIdx.x

get_global_size(0); size of NDRange in the x dimension

gridDim.x ×blockDim.x

get_local_size(0); Size of each work group in the x dimension

blockDim.x

Mapping of OpenCL Dimensions and Indices to CUDA

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Conceptual OpenCL Device Architecture

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OpenCL Memory Types CUDA Equivalent

global memory global memory

constant memory constant memory

local memory shared memory

private memory Local memory

Mapping OpenCL Memory Types to CUDA

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OpenCL Context for Device Management

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Structure of OpenCL main programGet information about platform and devices available on system

Select devices to use

Create an OpenCL command queue

Create memory buffers on device

Create kernel program object

Build (compile) kernel in-line (or load precompiled binary)

Create OpenCL kernel object

Set kernel arguments

Execute kernel

Read kernel memory and copy to host memory.

Transfer data from host to device memory buffers

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Platform

"The host plus a collection of devices managed by the OpenCL framework that allow an application to share resources and execute kernels on devices in the platform."

Platforms represented by a cl_platform object, initialized with clGetPlatformID()

http://opencl.codeplex.com/wikipage?title=OpenCL%20Tutorials%20-%201

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Simple code for identifying platform

//Platform

cl_platform_id platform;

clGetPlatformIDs (1, &platform, NULL);

List of OpenCL platforms found.

(Platform IDs)In our case just one platform, identified by &platform

Number of platform entries

Returns number of OpenCL platforms

available. If NULL, ignored.

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Context

“The environment within which the kernels execute and the domain in which synchronization and memory management is defined.

The context includes a set of devices, the memory accessible to those devices, the corresponding memory properties and one or more command-queues used to schedule execution of a kernel(s) or operations on memory objects.”

The OpenCL Specification version 1.1 http://www.khronos.org/registry/cl/specs/opencl-1.1.pdf

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//Context

cl_context_properties props[3];

props[0] = (cl_context_properties) CL_CONTEXT_PLATFORM;

props[1] = (cl_context_properties) platform;

props[2] = (cl_context_properties) 0;

cl_context GPUContext =

clCreateContextFromType(props,CL_DEVICE_TYPE_GPU,NULL,NULL,NULL);

//Context info

size_t ParmDataBytes;

clGetContextInfo(GPUContext,CL_CONTEXT_DEVICES,0,NULL,&ParmDataBytes);

cl_device_id* GPUDevices = (cl_device_id*)malloc(ParmDataBytes);

clGetContextInfo(GPUContext,CL_CONTEXT_DEVICES,ParmDataBytes,GPUDevices,N

ULL);

Code for context

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Command Queue

“An object that holds commands that will be executed on a specific device.

The command-queue is created on a specific device in a context.

Commands to a command-queue are queued in-order but may be executed in-order or out-of-order. ...”


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// Create command-queue

cl_command_queue GPUCommandQueue = clCreateCommandQueue(GPUContext,GPUDevices[0],0,NULL);

Simple code for creating a command queue

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Allocating memory on device

Use clCreatBuffer:

cl_mem clCreateBuffer(cl_context context,

cl_mem_flags flags,

size_t size,

void *host_ptr,

cl_int *errcode_ret)

OpenCL context, from clCreateContextFromType()

Bit field to specify type of allocation/usage (CL_MEM_READ_WRITE ,…)

No of bytes in buffer memory object

Returns error code if an error

Ptr to buffer data (May be previously allocated.)

Returns memory object

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Sample code for allocating memory on device for source data

// source data on host, two vectors

int *A, *B;A = new int[N];B = new int[N];for(int i = 0; i < N; i++) {

A[i] = rand()%1000;B[i] = rand()%1000;

}…

// Allocate GPU memory for source vectors

cl_mem GPUVector1 = clCreateBuffer(GPUContext,CL_MEM_READ_ONLY |CL_MEM_COPY_HOST_PTR,sizeof(int)*N, A, NULL);

cl_mem GPUVector2 = clCreateBuffer(GPUContext,CL_MEM_READ_ONLY |CL_MEM_COPY_HOST_PTR,sizeof(int)*N, B, NULL);

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Sample code for allocating memory on device for results on GPU

// Allocate GPU memory for output vector

cl_mem GPUOutputVector =

clCreateBuffer(GPUContext,CL_MEM_WRITE_ONLY,sizeof(int)*N, NULL,NULL);

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Kernel Program

Simple programs might be in the same file as the host code as our CUDA examples.

In that case need to formed into strings in a character array.

If in a separate file, can read that file into host program as a character string

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Kernel program

const char* OpenCLSource[] = {"__kernel void vectorAdd (const __global int* a,"," const __global int* b,"," __global int* c)","{"," unsigned int gid = get_global_id(0);"," c[gid] = a[gid] + b[gid];","}"};

…int main(int argc, char **argv){…}

If in same program as host, kernel needs to be strings (I think it can be a single string)

OpenCL qualifier to indicate kernel code

OpenCL qualifier to indicate kernel memory(Memory objects allocated from global memory pool)

Double underscores optional in OpenCL qualifiers

Returns global work-item ID in given dimension (0 here)

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Kernel in a separate file

// Load the kernel source code into the array source_str

FILE *fp; char *source_str; size_t source_size;

fp = fopen("vector_add_kernel.cl", "r"); if (!fp) { fprintf(stderr, "Failed to load kernel.\n"); exit(1); }

source_str = (char*)malloc(MAX_SOURCE_SIZE); source_size = fread( source_str, 1, MAX_SOURCE_SIZE, fp); fclose( fp );

http://mywiki-science.wikispaces.com/OpenCL

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Create kernel program object

const char* OpenCLSource[] = {…};

int main(int argc, char **argv)…

// Create OpenCL program object

cl_program OpenCLProgram = clCreateProgramWithSource(GPUContext,7,OpenCLSource,NULL,NULL);

Number of strings in kernel program array

Used if strings not null-terminated to given length of strings

Used to return error code if error

This example uses a single file for both host and kernel code. Can use clCreateprogramWithSource() with a separate kernel file read into host program

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Build kernel program

// Build the program (OpenCL JIT compilation)

clBuildProgram(OpenCLProgram,0,NULL,NULL,NULL,NULL);

Program object from clCreateProgramwithSource

Number of devices

List of devices, if more than one

Build options

Arguments for notification

routine

Function ptr to notification routine called with build complete. Then

clBuildProgram will return immediately,

otherwise only when build complete

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Creating Kernel Objects

// Create a handle to the compiled OpenCL function

cl_kernel OpenCLVectorAdd = clCreateKernel(OpenCLProgram, "vectorAdd", NULL);

Built prgram from clBuildProgram

Function name with __kernel qualifier

Return error code

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Set Kernel Arguments

// Set kernel arguments

clSetKernelArg(OpenCLVectorAdd,0,sizeof(cl_mem), (void*)&GPUVector1);

clSetKernelArg(OpenCLVectorAdd,1,sizeof(cl_mem), (void*)&GPUVector2);

clSetKernelArg(OpenCLVectorAdd,2,sizeof(cl_mem), (void*)&GPUOutputVector);

Kernel objectfrom clCreateKernel()

Which argument

Size of argument

Pointer to data for argument, from clCreateBuffer()

Number of events to complete

before this commands

Array describing no of global work

items

Array describing no of work items that make up a work

group

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Enqueue a command to execute kernel on device

// Launch the kernel

size_t WorkSize[1] = {N}; // Total number of work items size_t localWorkSize[1]={256}; //No of work items in work group

// Launch the kernel

clEnqueueNDRangeKernel(GPUCommandQueue,OpenCLVectorAdd,1,NULL,

WorkSize, localWorkSize, 0, NULL, NULL);

Kernel object from clCreatKernel()

Dimensions of work items

Offset used with work item

Event wait list

Event

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Function to copy from buffer object to host memory

The following function enqueue commands to read from a buffer object to host memory:

cl_int clEnqueueReadBuffer (cl_command_queue command_queue,cl_mem buffer,cl_bool blocking_read,size_t offset,size_t cb,void *ptr,cl_uint num_events_in_wait_list,const cl_event *event_wait_list,cl_event *event)


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Function to copy from host memory to buffer object

The following function enqueue commands to write to a buffer object from host memory:

cl_int clEnqueueWriteBuffer (cl_command_queue command_queue,cl_mem buffer,cl_bool blocking_write,size_t offset,size_t cb,const void *ptr,cl_uint num_events_in_wait_list,const cl_event *event_wait_list,cl_event *event)


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Copy data back from kernel

// Copy the output back to CPU memory

int *C;

C = new int[N];

clEnqueueReadBuffer(GPUCommandQueue,GPUOutputVector,CL_TRUE, 0, N*sizeof(int), C, 0, NULL, NULL);

Read is blocking

Byte offset in buffer

Size of data to read in bytes

Pointer to buffer in host to write

data

Number of events to

complete before this commands

Event wait list

Event

Command queue from clCreateCommandQueue

Device buffer from clCreateBuffer

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Results from GPU

cout << "C[“ << 0 << "]: " << A[0] <<"+"<< B[0] <<"=" << C[0] << "\n";

cout << "C[“ << N-1 << "]: “ << A[N-1] << "+“ << B[N-1] << "=" << C[N-

1] << "\n";

C++ here

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Clean-up

// Cleanup

free(GPUDevices);

clReleaseKernel(OpenCLVectorAdd);

clReleaseProgram(OpenCLProgram);

clReleaseCommandQueue(GPUCommandQueue);

clReleaseContext(GPUContext);

clReleaseMemObject(GPUVector1);

clReleaseMemObject(GPUVector2);

clReleaseMemObject(GPUOutputVector);

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Compiling

Need OpenCL header:

#include <CL/cl.h>

(For mac: #include <OpenCL/opencl.h> )

and link to the OpenCL library.

Compile OpenCL host program main.c using gcc, two phases:

gcc -c -I /path-to-include-dir-with-cl.h/ main.c -o main.ogcc -L /path-to-lib-folder-with-OpenCL-libfile/ -l OpenCL main.o -o host

Ref: http://www.thebigblob.com/getting-started-with-opencl-and-gpu-computing/

portability with opencl 1. high performance landscape high performance computing is trending towards...

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