opencl introduction an example for opencl lu oct.11 2014

OpenCL Introduction

AN EXAMPLE FOR OPENCLLU LU

OCT.11 2014

2OPENCL INTRODUCTION | APRIL 11, 2014

CONTENTS

1. Environment Configuration

2. Case Analyzing

1. ENVIRONMENT CONFIGURATION



IDE– Any IDE for C/C++ could use OpenCL.– We use Microsoft Visual Studio 2010.

Setting for the requiring projects:– Add include path of the SDK to Additional include directories.– Add library path of the SDK to Additional library directories.



Include Directory



Lib Directory



OpenCL Lib

2. CASE ANALYZING


2. CASE ANALYZING

1. Problem Description

2. Algorithm

3. Calculation Features

4. Parallelizing

5. Programming1. Kernel2. Host

6. Tools1. AMD Profiler2. gDEBugger


2.1 PROBLEM DESCRIPTION

Input an image, the rotation center and angle;

Output the rotated image with the same size of the input (original) image.

Original Rotated


2.2 ALGORITHM

Let be the rotation center, be the rotation angle;

A point in original image will be move into the new position after rotating clockwise as per following formula:


2.3 CALCULATION FEATURES

The calculation for each point is the same and independent;

A large amount of points.

So it is fit for parallel computing with GPU.


2.4 PARALLELIZING

With OpenCL framework, assign one work-item for the calculation for each point.

There are two methods to implement the algorithm:– Assign work-items as per original image;

• For each point, calculate the new position and copy it to the output image;• Write-memory conflict.

– Assign work-items as per output image.• For each point, calculate the source position and copy it from the original image;• Read-memory conflict.


2.5 PROGRAMMING

1. Kernel– which run in GPU.

2. Host– which run in CPU.


2.5.1 KERNEL

1. __kernel void image_rotate(

2. __global float * src_data, __global float * dest_data, //Data in global memory

3. int W, int H, //Image Dimensions

4. float sinTheta, float cosTheta ) //Rotation Parameters

5. {

6. //Thread gets its index within index space

7. const int ix = get_global_id(0);

8. const int iy = get_global_id(1);

9. //Calculate location of data to move into ix and iy– Output decomposition as mentioned

10. float xpos = (((float)ix) * cosTheta + ((float)iy) * sinTheta);

11. float ypos = (((float)iy) * cosTheta - ((float)ix) * sinTheta);

12. //Bound Checking

13. if ((((int)xpos >= 0) && ((int)xpos < W)) && (((int)ypos >= 0) && ((int)ypos < H)))

14. {

15. //Read (xpos,ypos) src_data and store at (ix,iy) in dest_data

16. dest_data[iy * W + ix] = src_data[(int)(floor(ypos * W + xpos))];

17. }

18. }


2.5.1 KERNEL

This kernel will rotate the image with rotation angle anticlockwise.

OpenCL defined some native function, such as sin and cos, but here calculate these value in host and pass them as parameters to the kernel because they are the same for every work-item.


2.5.1 KERNEL

KernelAnalyzer


2.5.1 KERNEL

KernelAnalyzer– We can see the bottlenecks are ALU ops.– It means that the main work of kernel is calculation, but not the data

transfer.– This kernel has high performance.


2.5.2 HOST

Platform

• Query Platform• Query Devices• Create Context• Create Command Queue

Compiler• Compile Program• Create Kernel

Runtime

• Create Buffers• Write buffers• Set Kernel Arguments• Run Kernel• Read buffers


2.5.2 HOST

Query Platformcl_int clGetPlatformIDs (cl_uint num_entries,

cl_platform_id *platforms,

cl_uint *num_platforms)

– This function is usually called twice; first calling is for getting the number of platform, and second calling is for getting the platforms.

– First calling:• clGetPlatformIDs(NULL, NULL, num)

– Second calling:• clGetPlatformIDs(num, platforms, NULL)

Query Platform

Query Devices

Create Context

Create Command Queue

Compile Program

Create Kernel

Create Buffers

Write buffers

Set Kernel Arguments

Run Kernel

Read buffers


2.5.2 HOST

Query Devicescl_int clGetDeviceIDs (cl_platform_id platform,

cl_device_type device_type,

cl_uint num_entries,cl_device_id

*devices,cl_uint

*num_devices)– This function is also usually called twice just like

clGetPlatformIDs.– device_type:

• CL_DEVICE_TYPE_ALL• CL_DEVICE_TYPE_CPU• CL_DEVICE_TYPE_GPU

Query Platform

Query Devices

Create Context


Compile Program

Create Kernel

Create Buffers

Write buffers


Run Kernel

Read buffers


2.5.2 HOST Query Platform

Query Devices

Create Context


Compile Program

Create Kernel

Create Buffers

Write buffers


Run Kernel

Read buffers


2.5.2 HOST

Create Contextcl_context clCreateContext (

const cl_context_properties *properties,cl_uint num_devices,const cl_device_id *devices,void (CL_CALLBACK *pfn_notify)(const

char *errinfo, const void *private_info, size_t cb, void *user_data),

void *user_data,cl_int *errcode_ret)

Create Command Queuecl_command_queue clCreateCommandQueue (

cl_context context,cl_device_id device,cl_command_queue_properties properties,cl_int *errcode_ret)

Query Platform

Query Devices

Create Context


Compile Program

Create Kernel

Create Buffers

Write buffers


Run Kernel

Read buffers



Query Devices

Create Context


Compile Program

Create Kernel

Create Buffers

Write buffers


Run Kernel

Read buffers

Compile Programcl_program clCreateProgramWithSource(

cl_context context,cl_uint count,const char **strings,const size_t *lengths,cl_int *errcode_ret)

Create Kernelcl_kernel clCreateKernel (

cl_program program,const char *kernel_name,cl_int *errcode_ret)



Query Devices

Create Context


Compile Program

Create Kernel

Create Buffers

Write buffers


Run Kernel

Read buffers

Create Bufferscl_mem clCreateBuffer (cl_context context,

cl_mem_flags flags,size_t size,void *host_ptr,cl_int *errcode_ret)

Write Bufferscl_int clEnqueueWriteBuffer (cl_command_queue command_queue,

cl_mem buffer,cl_bool blocking_write,size_t offset,size_t size,const void *ptr,cl_uint num_events_in_wait_list,const cl_event *event_wait_list,cl_event *event)



Query Devices

Create Context


Compile Program

Create Kernel

Create Buffers

Write buffers


Run Kernel

Read buffers

Set Kernel Arguments (for each one)cl_int clSetKernelArg (cl_kernel kernel,

cl_uint arg_index,size_t arg_size,const void *arg_value)

Run Kernelcl_int clEnqueueNDRangeKernel (cl_command_queue command_queue,

cl_kernel kernel,cl_uint work_dim,const size_t

*global_work_offset,const size_t *global_work_size,const size_t *local_work_size,cl_uint

num_events_in_wait_list,const cl_event *event_wait_list,cl_event *event)


2.5.2 HOST

Parameters of clEnqueueNDRangeKernel – work_dim is the number of dimensions used to

specify the global work-items and work-items in the work-group.

– global_work_offset can be used to specify an array of work_dim unsigned values that describe the offset used to calculate the global ID of a work-item.

– If global_work_offset is NULL, the global IDs start at offset (0, 0, … 0).

– local_work_size points to an array of work_dim unsigned values that describe the number of work-items that make up a work-group (also referred to as the size of the work-group) that will execute the kernel specified by kernel.

Query Platform

Query Devices

Create Context


Compile Program

Create Kernel

Create Buffers

Write buffers


Run Kernel

Read buffers


2.5.2 HOST

Parameters of clEnqueueNDRangeKernel– global_work_size into appropriate work-group

instances. If local_work_size is specified, global_work_size must be evenly divisible by local_work_size.

– event_wait_list and num_events_in_wait_list specify events that need to complete before this particular command can be executed.

– event returns an event object that identifies this particular kernel execution instance.

Query Platform

Query Devices

Create Context


Compile Program

Create Kernel

Create Buffers

Write buffers


Run Kernel

Read buffers


2.5.2 HOST

Read Bufferscl_int clEnqueueReadBuffer (

cl_command_queue command_queue,cl_mem buffer,cl_bool blocking_read,size_t offset,size_t size,void *ptr,cl_uint num_events_in_wait_list,const cl_event *event_wait_list,cl_event *event)

Query Platform

Query Devices

Create Context


Compile Program

Create Kernel

Create Buffers

Write buffers


Run Kernel

Read buffers


2.5.2 HOST

Release– clReleaseKernel– clReleaseProgram– clReleaseMemObject– clReleaseCommandQueue– clReleaseContext– clReleaseDevice


2.6 TOOLS

1. AMD Profiler

2. gDEBugger


2.6.1 AMD PROFILER

Counters

We can see the running information of any kernel.


2.6.1 AMD PROFILER

Trace

Trace the OpenCL Runtime.


2.6.2 GDEBUGGER

Debug into kernel


THANK YOU!


DISCLAIMER & ATTRIBUTION

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ATTRIBUTION

© 2013 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo and combinations thereof are trademarks of Advanced Micro Devices, Inc. in the United States and/or other jurisdictions. SPEC is a registered trademark of the Standard Performance Evaluation Corporation (SPEC). Other names are for informational purposes only and may be trademarks of their respective owners.

opencl introduction an example for opencl lu oct.11 2014

Documents

global float

kernel void image

main work of kernel

w xpos

rotated image

data transfer

location of data

image dimensionsfloat