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Posted 14 Feb 2012

Part 9: OpenCL Extensions and Device Fission

, 14 Feb 2012 CPOL
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This article discusses OpenCL extensions that provide programmers with additional capabilities such as double-precision arithmetic and Device Fission

The previous article, part 8, in this series on portable parallelism with OpenCL™ demonstrated how to incorporate OpenCL™ into heterogeneous workflows via a general-purpose "click-together tools" framework that can stream arbitrary messages (vectors, arrays, and arbitrary, complex nested structures) within a single workstation, across a network of machines, or within a cloud computing framework. The ability to create scalable workflows is important because data handling and transformation can be as complex and time consuming as the computational problem used to generate the desired result.

This article discusses OpenCL extensions that provide programmers with additional capabilities such as double-precision arithmetic and Device Fission. (Device Fission provides an interface to subdivide a single OpenCL device into multiple devices – each with a separate asynchronous command queues.)

OpenCL extensions can be defined by a vendor, a subset of the OpenCL working group, or by the entire OpenCL working group. The most portable extensions are those defined by the KHR extensions that are formally approved by the entire OpenCL working group while vendor extensions are the least portable and likely to be tied to a particular device or product line. Regardless of who provides the definition, no guarantee is made that an extension will be available on any platform.

Following are the three types of OpenCL extensions and naming conventions:

  • KHR extension: A KHR extension is formally ratified by the OpenCL working group and comes with a set of conformance tests to help ensure consistent behavior. KHR extensions are provided to support capabilities available on some but not all OpenCL devices. The Microsoft DirectX extension is one example of an important capability that is only available on devices that support Microsoft Windows. A KHR extension has a unique name of the form cl­_khr_<name>.
  • EXT extension: An EXT extension is developed by one or more of the OpenCL working group members. No conformance tests are required. It is reasonable to think of these as "work in process" extensions to assess usability, value, and portability prior to formal approval as a KHR extension. An EXT extension has a unique name of the form cl_ext_<name>.
  • Vendor extension: These extensions are provided by a vendor to expose features specific to a vendor device or product line. Vendor extensions should be considered as highly non-portable. The AMD device attribute query is one example that provides additional information about AMD devices. Vendor extensions are assigned a unique name of the form cl_<vendor>_<name>. Thus an AMD extension would have a name string cl_amd_<name>.

The #pragma OPENCL EXTENSION directive controls the behavior of the OpenCL compiler to allow or disallow extension(s). For example, part 4 of this series enabled double-precision computation on AMD devices with the following line. (Note cl_amd_fp64 can be updated to cl_khr_fp64 in the 2.6 AMD SDK release.)

#pragma OPENCL EXTENSION cl_amd_fp64 : enable
Example 1: Pragma to enable double-precision from part 4 of this series

The syntax of the extension pragma is:

#pragma OPENCL EXTENSION <extention_name> : <behavior>
Example 2: Form of an OpenCL extension pragma

The <behavior> token can one of the following:

  • enable: the extension is enabled if supported, or an error is reported if the specified extension is not supported or the token "all" is used.
  • disable: the OpenCL implementation/compiler behaves as if the specified extension does not exist.
  • all: only core functionality of OpenCL is used and supported, all extensions are ignored. If the specified extension is not supported then a warning is issued by the compiler.

By default, the compiler requires that all extensions be explicitly enabled as if it had been provided with the following pragma:

#pragma OPENCL EXTENSION all : disable
Example 3: Pragma to disable all extensions

The December 2011 version of the "AMD Accelerated Parallel Processing OpenCL" guide lists the availability of the following KHR extensions:

  • cl_khr_global_int32_base_atomics: basic atomic operations on 32-bit integers in global memory.
  • cl_khr_global_int32_extended_atomics: extended atomic operations on 32-bit integers in global memory.
  • cl_khr_local_int32_base_atomics: basic atomic operations on 32-bit integers in local memory.
  • cl_khr_local_int32_extended_atomics: extended atomic operations on 32-bit integers in local memory.
  • cl_khr_int64_base_atomics: basic atomic operations on 64-bit integers in both global and local memory.
  • cl_khr_int64_extended_atomics: extended atomic operations on 64-bit integers in both global and local memory.
  • cl_khr_3d_image_writes: supports kernel writes to 3D images.
  • cl_khr_byte_addressable_store: this eliminates the restriction of not allowing writes to a pointer (or array elements) of types less than 32-bit wide in kernel program.
  • cl_khr_gl_sharing: allows association of OpenGL context or share group with CL context for interoperability.
  • cl_khr_icd: the OpenCL Installable Client Driver (ICD) that lets developers select from multiple OpenCL runtimes which may be installed on a system.(This extension is automatically enabled as of SDK v2 for AMD Accelerated Parallel Processing.)
  • cl_khr_d3d10_sharing: allows association of D3D10 context or share group with CL context for interoperability.

Version 1.2 of the Khronos OpenCL API registry lists the availability of the following extensions. Either click on the hyperlink or access the Khronos OpenCL Working Group document, "The OpenCL Extension Specification"to find more detailed information about individual version 1.2 extensions.

Device Fission

By default, each OpenCL kernel attempts to use all the computing resources on a device according to a data parallel computing model. In other words, the same kernel is used to process data on all the computational resources in a device. In contrast, a task parallel model uses the available computing resources to run one or more independent kernels on the same device. Both task- and data-parallelism are valid ways to structure code to accelerate application performance given that some problems are better solved with task parallelism while others are more amenable to solution by data parallelism. In general task parallelism is more complicated to implement efficiently from both a software and hardware perspective. The default OpenCL default behavior to use all available computing resources according to a data-parallel model is a good one because it will provide the greatest speedup for individual kernels.

On AMD platforms, there are two methods to limit the number of cores utilized when running a kernel on a multi-core processor.

  1. The AMD OpenCL runtime checks an environmental variable CPU_MAX_COMPUTE_UNITS. If defined the AMD runtime will limit the number of processor cores used by an OpenCL application to the number specified by this variable. Simply set this environment variable to a number from 1 to the total number of multi-processor cores in a system. Note: this variable will not affect other devices such as GPUs nor is it guaranteed to work on with all vendor runtimes.
  2. The EXT Device Fission extension, cl_ext_device_fission, provides an interface within OpenCL to sub-divide a device into multiple sub-devices. The programmer can then create a command queue on each sub-device and enqueue kernels that run on only the resources (e.g. processor cores) within the sub-device. Each sub-device runs asynchronously to the other sub-devices. Currently, Device Fission only works for multi-core processors (both AMD and Intel) and the Cell Broadband engine. GPUs are not supported.

(Note: it is possible to restrict the number of work-groups and work-items so an OpenCL kernel uses only few cores of a multi-core processor and then rely on the operating system to schedule multiple applications to run efficiently. This method is not recommended for many reasons including the fact that it effectively hardcodes a purposely wasteful usage of resources. Further, this trick depends on external factors like the operating system to achieve efficient operation. Also, this trick will not work on GPUs.)

The webinar slides "Device Fission Extension for OpenCL" by Ben Gaster discusses Device Fission in the context of parallel pipelines for containers. He notes there are three general use cases when a user would like to subdivide a device:

  1. To reserve part of the device for use for high priority / latency-sensitive tasks.
  2. To more directly control the assignment of work to individual compute units.
  3. To subdivide compute devices along some shared hardware feature like a cache.

Typically these are use cases where some level of additional control is required to get optimal performance beyond that provided by standard OpenCL 1.1 APIs. Proper use of this interface assumes some detailed knowledge of the devices.

The AMD SDK samples provide an example that uses Device Fission on multi-core processors. In a standard install, this sample can be found in /opt/AMDAPP/samples/cl/app/DeviceFission. Ben Gaster also has some nice slides discussing the basics required to utilize Device Fission in his March 2011 presentation to the Khronos Group, "OpenCL Device Fission".

Device Fission in an OpenCL click-together tool framework

As noted in part 8 of this tutorial series, preprocessing the data can be as complicated and time consuming as the actual computation that generates the desired results. A "click-together" framework (illustrated below and discussed in greater detail in part 8) naturally exploits the parallelism of multi-core processors because each element in the pipeline is a separate application. The operating system scheduler ensures that any applications that have the data necessary to perform work will run – generally on separate processor cores. Under some circumstances, it is desirable to partition the workflow so that multiple click-together OpenCL applications can run on separate cores without interfering with each other. Perhaps the tasks are latency sensitive or the developer wishes to use a command like numactl under UNIX to bind an application to specific processing cores for better cache utilization.


Figure 1: Example click-together workflow

The following source code for from part 8 has been modified to use Device Fission. The changes are highlighted in color. Briefly, the changes are:

  1. Using a C++ define to enable the C++ Device Fission bindings.
  2. Check if the device supports the cl_ext_device_fission extension.
  3. Subdivide the device.
//Rob Farber

#include <span class="code-keyword"><cstdlib>
</span>#include <span class="code-keyword"><sys/types.h>
</span>#include <span class="code-keyword"><dlfcn.h>
</span>#include <span class="code-keyword"><string>
</span>#include <span class="code-keyword"><iostream>
</span>#include <span class="code-string">"packetheader.h"
#define PROFILING // Define to see the time the kernel takes
#define __NO_STD_VECTOR // Use cl::vector instead of STL version
#define __CL_ENABLE_EXCEPTIONS // needed for exceptions

// 1. enable the C++ bindings for Device Fission

#include <span class="code-keyword"><CL/cl.hpp>
</span>#include <span class="code-keyword"><fstream>
using namespace std;
void *lib_handle;
typedef char* (*initFini_t)(const char*, const char*, uint32_t*, uint32_t*);
typedef char* (*func_t)(const char*, const char*, uint32_t*, uint32_t*, char*);
typedef void (*dynFree_t)(char*);
typedef void (*oclSetup_t)(const char*, cl::CommandQueue*);
int main(int argc, char **argv) 
  if(argc < 3) {
    cerr << "Use: sourcefilename cpu|gpu oclSource" << endl;
    return -1;
  string base_filename(argv[1]);
  base_filename = base_filename.substr(0,base_filename.find_last_of("."));
  // build the shared object or dll

  string buildCommand("gcc -fPIC -shared -I $ATISTREAMSDKROOT/include ");
  buildCommand += string(argv[1]) 
    + string(" -o ") + base_filename + string(".so ");
  cerr << "Compiling with \"" << buildCommand << "\"" << endl;
  if(system(buildCommand.c_str())) {
    cerr << "compile command failed!" << endl;
    cerr << "Build command " << buildCommand << endl;
    return -1;
  // load the library -------------------------------------------------

  string nameOfLibToLoad("./");
  nameOfLibToLoad += base_filename;
  nameOfLibToLoad += ".so";
  lib_handle = dlopen(nameOfLibToLoad.c_str(), RTLD_LAZY);
  if (!lib_handle) {
    cerr << "Cannot load library: " << dlerror() << endl;
    return -1;
  // load the symbols -------------------------------------------------

  initFini_t dynamicInit= NULL;
  func_t dynamicFunc= NULL;
  initFini_t dynamicFini= NULL;
  dynFree_t dynamicFree= NULL;
  // reset errors

  // load the function pointers

  dynamicFunc= (func_t) dlsym(lib_handle, "func");
  const char* dlsym_error = dlerror();
  if (dlsym_error) { cerr << "sym load: " << dlsym_error << endl; return -1;}
  dynamicInit= (initFini_t) dlsym(lib_handle, "init");
  dlsym_error = dlerror();
  if (dlsym_error) { cerr << "sym load: " << dlsym_error << endl; return -1;}
  dynamicFini= (initFini_t) dlsym(lib_handle, "fini");
  dlsym_error = dlerror();
  if (dlsym_error) { cerr << "sym load: " << dlsym_error << endl; return -1;}
  dynamicFree= (dynFree_t) dlsym(lib_handle, "dynFree");
  dlsym_error = dlerror();
  if (dlsym_error) { cerr << "sym load: " << dlsym_error << endl; return -1;}
  // add a function to specify the ocl context and kernel file

  oclSetup_t oclSetupFunc;
  oclSetupFunc = (oclSetup_t) dlsym(lib_handle, "oclSetup");
  dlsym_error = dlerror();
  if (dlsym_error) { cerr << "sym load: " << dlsym_error << endl; return -1;}
  // -------------------------------------------------------------- 

  // Setup OCL context


  const string platformName(argv[2]);
  const char* oclKernelFile = argv[3];
  int ret= -1;
  cl::vector<int> deviceType;
  cl::vector< cl::CommandQueue > contextQueues;
  // crudely parse the command line arguments. 

  else if("gpu")==0) 
  else { cerr << "Invalid device type!" << endl; return(1); }
  // create the context and queues

  try {
    cl::vector< cl::Platform > platformList;
    // Get all the appropriate devices for the platform the

    // implementation thinks we should be using.

    // find the user-specified devices

    cl::vector<cl::Device> devices;
    for(int i=0; i < deviceType.size(); i++) {
      cl::vector<cl::Device> dev;
      platformList[0].getDevices(deviceType[i], &dev);
      // 2. check if the device supports Device Fission

      for(int j=0; j < dev.size(); j++) {
          find("cl_ext_device_fission") == std::string::npos) {
         cerr << "Device Fission NOT on device" << endl;
       } else 
         cerr << "Have DEVICE_FISSION" << endl;
      for(int j=0; j < dev.size(); j++) devices.push_back(dev[j]);
    // set a single context

    cl_context_properties cprops[] = {CL_CONTEXT_PLATFORM, NULL, 0};
    cl::Context context(devices, cprops);
    cerr << "Using the following device(s) in one context" << endl;
    for(int i=0; i < devices.size(); i++)  {
      cerr << "  " << devices[i].getInfo<CL_DEVICE_NAME>() << endl;
    // Create the separate command queues to perform work

    // 3. Device Fission : equally sub-divide the device

    cl_device_partition_property_ext props[] = {
    cl::vector<cl::Device> sdevices;
    devices[0].createSubDevices(props, &sdevices);
    cerr << "Sub-divided into " << sdevices.size() << " devices" << endl;
    for(int i=0; i < sdevices.size(); i++)  {
      cl::CommandQueue queue(context, sdevices[i],CL_QUEUE_PROFILING_ENABLE);
      cl::CommandQueue queue(context, sdevices[i],0);
      contextQueues.push_back( queue );
  } catch (cl::Error error) {
    cerr << "caught exception: " << error.what() 
        << '(' << error.err() << ')' << endl;
  oclSetupFunc(oclKernelFile, &contextQueues[0]);
  // -------------------------------------------------------------- 

  // work with protobufs


  //enable C++ binary cin and cout

  if (!setPacket_binaryIO()) {
     cerr << "Cannot set binary mode for cin and cout!" << endl;
     return -1;
  uint32_t size, type;
  char *retBlob;
  // handle initialization and put information on output stream when told

  if( (retBlob=(*dynamicInit)(argv[0], base_filename.c_str(),&size, &type)) ) {
    writePacketHdr(size, type, &std::cout);
    cout.write(retBlob, size);
  // read stream from cin and put information on output stream when told

  while(readPacketHdr(&size, &type, &std::cin)) {
    char *blob = new char[size];, size);
    retBlob =(*dynamicFunc)(argv[0], base_filename.c_str(), &size, &type, blob);
    if(retBlob) {
      writePacketHdr(size, type, &std::cout);
      cout.write(retBlob, size);
      // optimization: if retBlob == blob then allocated was by this program

      if(retBlob != blob) (dynamicFree)(retBlob);
    delete [] blob;
  // handle finalization (fini) and put information on output stream when told

  if( retBlob = (*dynamicFini)(argv[0], base_filename.c_str(),&size, &type) ) {
    writePacketHdr(size, type, &std::cout);
    cout.write(retBlob, size);
  // unload the library -----------------------------------------------

  return 0;
Example 4: Modified to use Device Fission

To build and use this example, simply substitute this source code for in part 8.

For testing purposes, the following OpenCL kernel,, can be substituted for in the part 8 commands to demonstrate that Device Fission is working.

inline __kernel void init(int veclen, __global TYPE1* c, int offset)

inline __kernel void func(int veclen, __global TYPE1* c, int offset)
  // get the index of the test we are performing

  int index = get_global_id(0);

  // loop performing busywork to show processor activity

  int n=100000;
  for(int j=0; j < n; j++)
  for(int i=0; i < n; i++) {
    TYPE1 tmp = c[index + offset*veclen];
    c[index + offset*veclen] += c[index + offset*veclen];
    c[index + offset*veclen] -= tmp;

inline __kernel void fini(int veclen, __global TYPE1* c, int offset)
Example 5: Source for to consume lots of CPU time

Following is the graphical output of system monitor for a 6-core AMD Phenom™ II X6 1055T Processor running Ubuntu 10.10 that demonstrates the default OpenCL behavior to run on all the cores. As noted previously, the source code was substituted for in the part 8 script. Notice that the processor utilization jumps for all six processors when the application starts running.


Example 6: Default OpenCL behavior is to use all processing cores

Utilizing the Device Frission version of from this tutorial, we see that only one processing core (in this case the orange line) achieves high utilization.


Example 7: Only a single core is used by the Device Fission code


OpenCL extensions provide programmers with additional capabilities such as double-precision arithmetic and Device Fission. Vendor extensions are the least portable but they do provide an important path to expose an API to exploit device capabilities. The KHR extensions are the most general as they require formal ratification and a test suite to define a standard behavior. The EXT extensions can be viewed as a "work in progress" API that might eventually achieve the formal status of a KHR extension.

With the Device Fission extension, programmers have an API to subdivide multi-core processors to better exploit system capabilities. The Google protobuf streaming framework introduced in part 8 was easily extended to utilize Device Fission. Through operating systems commands such as numactl, programmers can even bind OpenCL applications in this streaming framework to specific processing cores. By extension, OpenCL application programmers can use Device Fission to further optimize OpenCL plugins and generic workflows discussed in parts 7 and 8 of this tutorial series.


This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


About the Author

United States United States
Rob Farber is a senior scientist and research consultant at the Irish Center for High-End Computing in Dublin, Ireland and Chief Scientist for BlackDog Endeavors, LLC in the US.

Rob has been on staff at several US national laboratories including Los Alamos National Laboratory, Lawrence Berkeley National Laboratory, and at Pacific Northwest National Laboratory. He also served as an external faculty member at the Santa Fe Institute, co-founded two successful start-ups, and has been a consultant to Fortune 100 companies. His articles have appeared in Dr. Dobb's Journal and Scientific Computing, among others. He recently completed a book teaching massive parallel computing.

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