Java pitfall: How to prevent Runtime.getRuntime().exec() from hanging

Runtime.getRuntime().exec() is used to execute a command line program from within the Java program as below.

import java.io.File;
import java.io.IOException;

public class ProcessExecutor {

    public static void main(String[] args) throws IOException, InterruptedException {

        String command = "c:\my.exe";
        String workingDir = "c:\myworkingdir";

        // start execution
        Process process = Runtime.getRuntime().exec(command, null, new File(workingDir));

        // wait for completion
        process.waitFor();

    }

}

However the command line program being run above may block/deadlock as it did for me on Windows 7. I was trying to run a program that produced a lot of output. I could run the program standalone but through Java it hung indefinitely. Thread dumps showed nothing.

After being quite puzzled for a while as to why this was happening finally I found the answer in Java 7 api docs for Process.

Because some native platforms only provide limited buffer size for standard input and output streams, failure to promptly write the input stream or read the output stream of the subprocess may cause the subprocess to block, or even deadlock.

So, in fact, the fix for the above program is as follows.

import java.io.BufferedInputStream;
import java.io.File;
import java.io.IOException;

public class ProcessExecutor {

    public static void main(String[] args) throws IOException, InterruptedException {

        String command = "c:\my.exe";
        String workingDir = "c:\myworkingdir";

        // start execution
        Process process = Runtime.getRuntime().exec(command, null, new File(workingDir));

        // exhaust input stream
        BufferedInputStream in = new BufferedInputStream(process.getInputStream());
        byte[] bytes = new byte[4096];
        while (in.read(bytes) != -1) {}

        // wait for completion
        process.waitFor();

    }

}

This is so bad. Not only is this unexpected but it is also undocumented in the exec call. Also another problem is that if you are timing the total execution time for a given command and don’t care about the output you need to read the output anyway and probably subtract the reading time from the total execution time. I’m not sure how accurate that will be.

Surely there could have been a better way to handle this for the user in the api internals. So windows 7 must be one of those OSs with small buffer sizes then. Anyway, at least you know now. Obviously you don’t have to read it into nothing as I’m doing above. You can write it to stdout or a file.

Update: A commenter made a good point that I’d forgotten to read the error stream above. Don’t forget to do so in your own code!

OpenCL Cookbook: How to leverage multiple devices in OpenCL

So far, in the OpenCL Cookbook series, we’ve only looked at utilising a single device for computation. But what happens when you install more than one card in your host machine? How do you scale your computation across multiple GPUs? Will your code automatically scale to multiple devices or does it require you to consciously think about how to distribute the load of the computation across all available devices and change your code to apply that strategy? Here I look at answers to these questions.

Decide on how you want to use the host binding to support multiple devices

There are two ways in which a given host binding can support multiple devices.

  • A single context across all device and one command queue per device.
  • One context and command queue per device

Let’s look at these in more detail with skeletal implementations in C.

Creating a single context across all devices and one command queue per device

For this particular way of the binding supporting multiple devices we create only one context and share it across one command queue per device. So if we have say two devices we’ll have one context and two command queues each of which share that one context.

#include <iostream>
#include <CL/cl.hpp>
#include <CL/opencl.h>

int main () {

    cl_int err;
    
    // get first platform
    cl_platform_id platform;
    err = clGetPlatformIDs(1, &platform, NULL);
    
    // get device count
    cl_uint deviceCount;
    err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 0, NULL, &deviceCount);
    
    // get all devices
    cl_device_id* devices;
    devices = new cl_device_id[deviceCount];
    err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, deviceCount, devices, NULL);
    
    // create a single context for all devices
    cl_context context = clCreateContext(NULL, deviceCount, devices, NULL, NULL, &err);
    
    // for each device create a separate queue
    cl_command_queue* queues = new cl_command_queue[deviceCount];
    for (int i = 0; i < deviceCount; i++) {
        queues[i] = clCreateCommandQueue(context, devices[i], 0, &err);
    }

    /*
     * Here you have one context across all devices and one command queue per device.
     * You can choose to send your tasks to any of these queues depending on which
     * device you want to execute the task on.
     */

    // cleanup
    for(int i = 0; i < deviceCount; i++) {
        clReleaseDevice(devices[i]);
        clReleaseCommandQueue(queues[i]);
    }
    
    clReleaseContext(context);

    delete[] devices;
    delete[] queues;
    
    return 0;
    
}

Creating one context and one command queue per device

Here I create one context and one command queue per device each of which have their own context rather than sharing one.

#include <iostream>
#include <CL/cl.hpp>
#include <CL/opencl.h>

int main () {

    cl_int err;
    
    // get first platform
    cl_platform_id platform;
    err = clGetPlatformIDs(1, &platform, NULL);
    
    // get device count
    cl_uint deviceCount;
    err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 0, NULL, &deviceCount);
    
    // get all devices
    cl_device_id* devices;
    devices = new cl_device_id[deviceCount];
    err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, deviceCount, devices, NULL);
    
    // for each device create a separate context AND queue
    cl_context* contexts = new cl_context[deviceCount];
    cl_command_queue* queues = new cl_command_queue[deviceCount];
    for (int i = 0; i < deviceCount; i++) {
        contexts[i] = clCreateContext(NULL, deviceCount, devices, NULL, NULL, &err);
        queues[i] = clCreateCommandQueue(contexts[i], devices[i], 0, &err);
    }

    /*
     * Here you have one context and one command queue per device.
     * You can choose to send your tasks to any of these queues.
     */

    // cleanup
    for(int i = 0; i < deviceCount; i++) {
        clReleaseDevice(devices[i]);
        clReleaseContext(contexts[i]);
        clReleaseCommandQueue(queues[i]);
    }
    
    delete[] devices;
    delete[] contexts;
    delete[] queues;
    
    return 0;

}

How do you scale your computation across multiple devices?

The process of utilising multiple devices for your computation is not done automatically by the binding when new devices are detected sadly. Nor is it possible for it do so. Doing this requires active thought from the host programmer. When using a single device you send all your kernel invocations to the command queue associated with that device. In order to use multiple devices you must have one command queue per device either sharing a context or each queue having its own context. Then you must decide how to distribute your kernel calls across all available queues. It may be as simple as a round robin strategy across all queues for all your computations or it may be more complex.

Bear in mind that if your computation entails reading back a result synchronously then a round robin strategy across queues won’t work. This is because each current call will block and complete prior to you sending to the next queue which will essentially make the process of distributing across queues serial. Obviously this defeats the whole purpose of having multiple devices operating in parallel. What you really need is one host thread per device each sending computations to its own command queue. That way each queue is receiving and processing computations in parallel with other queues. Then you effectively achieve true hardware parallelism.

Which of the two ways should you use?

It depends. I would try the single context option first as it’s likely to use less memory and be faster. If you encounter instability or problems I would switch to the multiple context method. That’s the general rule. There is, however, another reason you may opt for a multiple context method. If you are using multiple threads which all require access to a context it is preferable for each thread to have its own context as the opencl host binding is not guaranteed to be thread safe. If you try to access a single context across multiple threads you may get serious system crashes and reboots so always have thread confined opencl structures.

Using a single context across multiple host threads

You may want to use one thread per device to send tasks to the command queue associated with each device. In this case you will have multiple host threads. But here have to be careful. In my experience it has not been safe to use a single context across multiple host threads. The last time I tried this was in C# using the Cloo host binding. Using a single context across multiple host threads resulted in a Windows 7 blue screen, Windows dumping memory to a file and then rebooting after which Windows failed to come back up until physically rebooted once more from the machine. The solution is to use the multi context option outlined above. Have thread confined separation for opencl resources and you’ll be fine.

OpenCL Cookbook: Hello World using C# Cloo host binding

So far I’ve used the C and C++ bindings in the OpenCL Cookbook series. This time I provide a quick and simple example of how to use Cloo – the C# OpenCL host binding. However, since Cloo, for whatever reason, didn’t work as expected with a char array I will use an integer array instead. In other words – instead of sending a “Hello World!” message to the kernel I will send five integers instead. My guess is that there is some sort of bug with Cloo and char arrays.

Device code using Cloo’s variant of the OpenCL language

kernel void helloWorld(global read_only int* message, int messageSize) {
	for (int i = 0; i < messageSize; i++) {
		printf("%d", message[i]);
	}
}

The kernel above is merely illustrative in that it simply receives an integer array and its size and prints the array.

Note that the OpenCL syntax here is not the same as in C/C++. It has additional keywords to say whether the arguments are read only or write or read write and the kernel keyword is not prefixed with two underscores. The Cloo author must have decided that the original OpenCL syntax was for whatever reason unsuitable for adoption which IMO was a mistake. The OpenCL language syntax should be standard for portability, reusability and also so that there is only a single learning curve.

Host code using Cloo API

using System;
using System.Collections.Concurrent;
using System.Threading.Tasks;
using System.IO;
using Cloo;

namespace test
{
    class Program
    {
        static void Main(string[] args)
        {
            // pick first platform
            ComputePlatform platform = ComputePlatform.Platforms[0];

            // create context with all gpu devices
            ComputeContext context = new ComputeContext(ComputeDeviceTypes.Gpu,
                new ComputeContextPropertyList(platform), null, IntPtr.Zero);

            // create a command queue with first gpu found
            ComputeCommandQueue queue = new ComputeCommandQueue(context,
                context.Devices[0], ComputeCommandQueueFlags.None);

            // load opencl source
            StreamReader streamReader = new StreamReader("..\..\kernels.cl");
            string clSource = streamReader.ReadToEnd();
            streamReader.Close();

            // create program with opencl source
            ComputeProgram program = new ComputeProgram(context, clSource);

            // compile opencl source
            program.Build(null, null, null, IntPtr.Zero);

            // load chosen kernel from program
            ComputeKernel kernel = program.CreateKernel("helloWorld");

            // create a ten integer array and its length
            int[] message = new int[] { 1, 2, 3, 4, 5 };
            int messageSize = message.Length;

            // allocate a memory buffer with the message (the int array)
            ComputeBuffer<int> messageBuffer = new ComputeBuffer<int>(context,
                ComputeMemoryFlags.ReadOnly | ComputeMemoryFlags.UseHostPointer, message);

            kernel.SetMemoryArgument(0, messageBuffer); // set the integer array
            kernel.SetValueArgument(1, messageSize); // set the array size

            // execute kernel
            queue.ExecuteTask(kernel, null);

            // wait for completion
            queue.Finish();
        }
    }
}

The C# program above uses the Cloo object oriented api to interface with the underlying low level opencl implementation. It’s pretty self explanatory if you’ve been following the series so far. The output of the program is 12345.

How to use core affinity to pin a process to a core on Windows using C#

Previously I wrote about how to use core affinity to pin a process to a core on Windows using C/C++. This is just a quick note on how to do so in C#. It’s actually a one liner so much easier than C/C++ unsurprisingly.

System.Diagnostics.Process.GetCurrentProcess().ProcessorAffinity = (System.IntPtr)(1 << coreId);

Above the coreId is a zero indexed number of the core you’d like to pin to. If you pass a coreId that’s incorrect this line of code will fail as below so there’s no need for explicit error checks.

Unhandled Exception: System.ComponentModel.Win32Exception: The parameter is incorrect

In case you’re wondering why anyone would want to limit themselves to one core in a multicore world it’s useful for checking how well single threaded processes perform when running one process per core and how well they scale as the number of processes goes up.

For example you may have 16 cores and because your process is single threaded you may want to run 16 processes each pinned to its respective core. As you deploy more and more processes however you may experience a degradation in how long each takes to perform a set amount of work. This will usually be due to cache overflow and reaching memory bandwidth limitations.

How to use core affinity to pin a process to a core on Windows using C/C++

Here’s how you can use core affinity to pin a particular process to any given core in C/C++ on Windows. The program below works by receiving the core number to pin the process to as the first argument to the executable. So for the first core you’d pass 0, for core 16 you’d pass 15 and so on.

#include <windows.h>
#include <iostream>
#include <algorithm>

using namespace std;

int main (int argc, char **argv) {

    // pin process to a core requested by incoming argument
    BOOL result = SetProcessAffinityMask(GetCurrentProcess(), 1 << atoi(argv[1]));
    if (result == 0) { cout << "SetProcessAffinityMask failed" << endl; return -1; }
    
    // perform long running cpu computation
    for (int i = 0; i < 10; i++) {
    
        // create a large array
        int sampleSize = 100000000;
        int* randoms = new int[sampleSize];
        for (int i = 0; i < sampleSize; i++) {
            randoms[i] = rand();
        }
        
        // sort it to take up some cpu time
        sort(randoms, randoms + sizeof randoms / sizeof randoms[0]);
        
        // cleanup
        delete[] randoms;
        
    }
    
    return 0;
    
}

The line of interest that actually applies core affinity is below.

BOOL result = SetProcessAffinityMask(GetCurrentProcess(), 1 << atoi(argv[1]));

There’s one thing you should watch out for when using core affinity. If you run a long running computation like the one above and you’re checking which cores get spiked on task manager > performance like I was you may see that more than one core gets spiked and you may initially think that the core affinity is not working. However this is a red herring as I explain below.

You’ll notice that other cores will suffer small spikes but only one core (the one you are pinning to) will sustain load the entire duration of the computation. So what’s actually happening is that in the initial period when your program is being set up to run other cores are getting involved but for the execution of your program only the requested core is being used. So always make sure that the computation runs long enough for you to see which one runs under sustained load.

Concurrent producer consumer pattern using C# 4.0, BlockingCollection & Tasks

Here is a very simple illustrative and annotated producer consumer pattern example using C#, .NET 4.0, BlockingCollection and Tasks. The example sets up one producer thread on which it produces 100 integers and two consumer threads which each read that data off a common concurrent blocking queue. Although I use a BlockingCollection here it is backed internally by a concurrent queue.

using System;
using System.Collections.Concurrent;
using System.Threading.Tasks;

namespace test
{

    class Program
    {

        static void Main(string[] args)
        {

            Console.WriteLine();

            // declare blocking collection backed by concurrent queue
            BlockingCollection<int> b = new BlockingCollection<int>();

            // start consumer 1 which waits for data until available
            var consumer1 = Task.Factory.StartNew(() =>
            {
                foreach (int data in b.GetConsumingEnumerable())
                {
                    Console.Write("c1=" + data + ", ");
                }
            });

            // start consumer 2 which waits for data until available
            var consumer2 = Task.Factory.StartNew(() =>
            {
                foreach (int data in b.GetConsumingEnumerable())
                {
                    Console.Write("c2=" + data + ", ");
                }
            });

            // produce 100 integers on a producer thread
            var producer = Task.Factory.StartNew(() =>
            {
                for (int i = 0; i < 100; i++)
                {
                    b.Add(i);
                }
                b.CompleteAdding();
            });

            // wait for producer to finish producing
            producer.Wait();
            
            // wait for all consumers to finish consuming
            Task.WaitAll(consumer1, consumer2);

            Console.WriteLine();
            Console.WriteLine();

        }

    }

}

The output from running the program above is as follows. It’s basically consumer 1 and 2 interleaving as they read integers off the queue as they’re being produced.

c1=0, c1=2, c1=3, c1=4, c1=5, c1=6, c1=7, c1=8, c1=9, c1=10, c1=11, c1=12, c1=13, c1=14, c1=15, c1=16, c1=17, c1=18, c1=19, c2=1, c2=21, c2=22, c2=23, c2=24, c2
=25, c2=26, c2=27, c2=28, c2=29, c2=30, c2=31, c2=32, c2=33, c2=34, c2=35, c2=36, c2=37, c2=38, c2=39, c2=40, c2=41, c2=42, c2=43, c2=44, c2=45, c2=46, c2=47, c
2=48, c2=49, c2=50, c2=51, c2=52, c2=53, c2=54, c2=55, c2=56, c2=57, c2=58, c2=59, c2=60, c2=61, c2=62, c2=63, c2=64, c2=65, c2=66, c2=67, c2=68, c2=69, c2=70,
c2=71, c2=72, c2=73, c2=74, c2=75, c2=76, c2=77, c2=78, c2=79, c2=80, c2=81, c2=82, c2=83, c2=84, c2=85, c2=86, c2=87, c2=88, c2=89, c2=90, c2=91, c2=92, c2=93,
 c2=94, c2=95, c2=96, c2=97, c2=98, c2=99, c1=20,

It’s somewhat sad and disheartening to see just how much further ahead C# is of Java in terms of the expressiveness and richness of the language. I personally really enjoyed using the lambdas above and I hope that with Java 8 the SDK libraries will grow to develop a more fluid and expressive style.

Credit: [1, 2]

OpenCL Cookbook: Parallelise your host loops using OpenCL

Continuing on in our series – this time we look at possibly the most important topic of all in OpenCL. It is the reason why we use OpenCL and it is also the most compelling benefit that OpenCL offers. It is, of course, parallelism. But how do we exploit the vast amount of parallelism that GPUs offer? At the simplest level we can do so by exploiting latent areas of parallelism in our host code the simplest of which are loops. In other words – if we can port loops in our host code to the GPU they become parallel and get faster by a factor of the total number of iterations. I demonstrate using a small example.

Host loop

void cpu_3d_loop (int x, int y, int z) {

    for (int i = 0; i < x; i++) {
        for (int j = 0; j < y; j++) {
            for (int k = 0; k < z; k++) {
                printf("CPU %d,%d,%dn", i, j, k);
            }
        }
    }

}

Imagine the loop above in our C++ host code. This is not one loop but in fact three. In other words it has three dimensions. The total number of iterations in this combined loop is x*y*z. If x=4, y=3 and z=2 the total number of iterations would be 4x3x2=24. On the CPU these loops execute serially which is fine for a small number of iterations but for large numbers it becomes a fundamental bottleneck. If this set of loops was ported to the GPU each iteration would run in parallel and the total number of threads in use would be 24 for the previous example.

A small scale example may not seem impressive at first. You could argue that you could just as well run 24 threads on the CPU. But consider this: what happens when you have the above set of loops in your host code performing thousands or even millions of iterations? How are you going to achieve hardware parallelism in this case on the CPU? The answer is you can’t. GPUs each have hundreds of cores and offer a far greater degree of parallelism so loops with a large number of iterations becomes easy work for the GPU which can run thousands or even millions of threads effectively. Below I demonstrate how to port such a loop to OpenCL.

Host binding code

#define __NO_STD_VECTOR
#define __CL_ENABLE_EXCEPTIONS

#include <fstream>
#include <iostream>
#include <iterator>
#include <CL/cl.hpp>
#include <CL/opencl.h>

using namespace cl;

void cpu_3d_loop (int x, int y, int z) {

    for (int i = 0; i < x; i++) {
        for (int j = 0; j < y; j++) {
            for (int k = 0; k < z; k++) {
                printf("CPU %d,%d,%dn", i, j, k);
            }
        }
    }

}

int main () {

    // CPU 3d loop

    int x = 4;
    int y = 3;
    int z = 2;
    cpu_3d_loop(x, y, z);
    std::cout << std::endl;

    // GPU 3d loop

    vector<Platform> platforms;
    vector<Device> devices;
    vector<Kernel> kernels;
    
    try {
    
        // create platform, context and command queue
        Platform::get(&platforms);
        platforms[0].getDevices(CL_DEVICE_TYPE_GPU, &devices);
        Context context(devices);
        CommandQueue queue(context, devices[0]);

        // load opencl source
        std::ifstream cl_file("kernels.cl");
        std::string cl_string(std::istreambuf_iterator<char>(cl_file),
            (std::istreambuf_iterator<char>()));
        Program::Sources source(1, std::make_pair(cl_string.c_str(), 
            cl_string.length() + 1));

        // create program and kernel and set kernel arguments
        Program program(context, source);
        program.build(devices);
        Kernel kernel(program, "ndrange_parallelism");

        // execute kernel and wait for completion
        NDRange global_work_size(x, y, z);
        queue.enqueueNDRangeKernel(kernel, NullRange, global_work_size, NullRange);
        queue.finish();

    } catch (Error e) {
        std::cout << std::endl << e.what() << " : " << e.err() << std::endl;
    }

    return 0;
    
}

The above program runs the cpu loop and then runs the equivalent logic on the gpu. Both cpu and gpu runs produce output to show which iteration they are processing. The key lines of code that demonstrate how to port the loop are below.

NDRange global_work_size(x, y, z);
queue.enqueueNDRangeKernel(kernel, NullRange, global_work_size, NullRange);

Here we set three upper bounds – one for each loop – this is known as the global work size. The kernel can then retrieve values for the currently executing iteration within the kernel itself as shown below. It can then use these indices to do whatever work is inside the loop. In this case we just print the indices for illustration.

Kernel code

The kernel you see below is executed x*y*z times with different values for i, j and k. See? No loops! 🙂

__kernel void ndrange_parallelism () {

	int i = get_global_id(0);
	int j = get_global_id(1);
	int k = get_global_id(2);

	printf("GPU %d,%d,%dn", i, j, k);
	
}

The output of running the above host code is as follows.

CPU 0,0,0
CPU 0,0,1
CPU 0,1,0
CPU 0,1,1
CPU 0,2,0
CPU 0,2,1
CPU 1,0,0
CPU 1,0,1
CPU 1,1,0
CPU 1,1,1
CPU 1,2,0
CPU 1,2,1
CPU 2,0,0
CPU 2,0,1
CPU 2,1,0
CPU 2,1,1
CPU 2,2,0
CPU 2,2,1
CPU 3,0,0
CPU 3,0,1
CPU 3,1,0
CPU 3,1,1
CPU 3,2,0
CPU 3,2,1

GPU 0,0,0
GPU 1,0,0
GPU 2,0,0
GPU 3,0,0
GPU 0,1,0
GPU 1,1,0
GPU 2,1,0
GPU 3,1,0
GPU 0,2,0
GPU 1,2,0
GPU 2,2,0
GPU 3,2,0
GPU 0,0,1
GPU 1,0,1
GPU 2,0,1
GPU 3,0,1
GPU 0,1,1
GPU 1,1,1
GPU 2,1,1
GPU 3,1,1
GPU 0,2,1
GPU 1,2,1
GPU 2,2,1
GPU 3,2,1

NOTE: Although there may appear to be a sequence in the order in which the GPU processes the iterations this is only due to the use of printf(). In reality when not using printf() the order of iterations is completely arbitrary and random. Therefore one must not rely on the order of iterations when porting loops to the GPU. If you need loops to be in a certain order then you can either keep your loops on the host or port only those parts of the loop that do not need to be sequential.

Why use GPU computing?

Although this example is fairly simple it does illustrate the most important value add of GPU computing and OpenCL. Hardware parallelism is the essence of what GPU computing offers and it is the most compelling reason to use it. If you imagine a legacy codebase and all the latent areas of parallelism that are currently running sequentially you can imagine the vast untapped power of GPGPU. Later on in the series we will look at techniques to port existing host code to the GPU. That process can be very difficult but can provide dramatic gains in performance far beyond the limits of CPU computing. Till next time.

OpenCL Cookbook: Hello World using C++ host binding

Last time, in the OpenCL Cookbook series, I presented a hello world example using OpenCL and C for the host binding language. This time I present a very similar example but using the C++ host binding language. As you already know from previous posts the host language that interfaces with an OpenCL device can be any number of languages such as C, C++, Java, C# and Python.

So far I’ve been using the C API but I’ve decided to switch to the C++ API for two reasons: (1) it’s considerably less lines of code being more succinct and (2) it supports exceptions meaning that you do not have to check error codes for every line of binding code that you write. So, here follows, a brief primer of the C++ OpenCL binding. It’s a very simple example but trust me – we’ll be getting to more complex examples soon (time is the issue).

OpenCL kernel

__kernel void hello_world (__global char* message, int messageSize) {
	for (int i =0; i < messageSize; i++) {
		printf("%s", message[i]);
	}
}

The kernel (OpenCL function) above receives a char array (in essence a string) from the host as well as the size of the char array (as there is no way to derive an array's size from the array itself (Java programmers gasp in shock and disgust). The kernel simply iterates over all the letters in the char array and prints them one at a time to standard output thereby printing the message: "Hello World!". Now let's look at the C++ code that interfaces with this kernel.

C++ host binding

#define __CL_ENABLE_EXCEPTIONS

#include <fstream>
#include <iostream>
#include <iterator>
#include <CL/cl.hpp>
#include <CL/opencl.h>

using namespace std;

int main () {

    vector<cl::Platform> platforms;
    vector<cl::Device> devices;
    vector<cl::Kernel> kernels;
    
    try {
    
        // create platform
        cl::Platform::get(&platforms);
        platforms[0].getDevices(CL_DEVICE_TYPE_GPU, &devices);

        // create context
        cl::Context context(devices);

        // create command queue
        cl::CommandQueue queue(context, devices[0]);

        // load opencl source
        ifstream cl_file("opencl_hello_world.cl");
        string cl_string(istreambuf_iterator<char>(cl_file), (istreambuf_iterator<char>()));
        cl::Program::Sources source(1, make_pair(cl_string.c_str(), 
            cl_string.length() + 1));

        // create program
        cl::Program program(context, source);

        // compile opencl source
        program.build(devices);

        // load named kernel from opencl source
        cl::Kernel kernel(program, "hello_world");

        // create a message to send to kernel
        char* message = "Hello World!";
        int messageSize = 12;

        // allocate device buffer to hold message
        cl::Buffer buffer(CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, 
            sizeof(char) * messageSize, message);

        // set message as kernel argument
        kernel.setArg(0, buffer);
        kernel.setArg(1, sizeof(int), &messageSize);

        // execute kernel
        queue.enqueueTask(kernel);

        // wait for completion
        queue.finish();

        cout << endl;
        
    } catch (cl::Error e) {
        cout << endl << e.what() << " : " << e.err() << endl;
    }
    
    return 0;
    
}

The above C++ host binding code is annotated to say what it's doing at each step but I'll provide a brief overview. Initially it's creating a platform, a context and a command queue which are basic opencl binding data structures that are required to interface with an opencl device. It then loads the opencl source from a separate file and with it creates a program. The program is built which compiles the opencl source. It then loads a specific kernel (function) from that source by a given name. It creates a string message on the host side but in order to send it to the device it must create a buffer of the same size as the message. The buffer is created and set as a kernel argument along with the size of the message we are sending.

The kernel is then executed and we wait for its completion on the host. The finish command flushes all outstanding tasks to the device and waits for them to finish. Note the clean exception handling using a try/catch wrap around the entire code instead of having to check error codes produced by each statement. I much prefer the C++ api to the C API. I think you'll agree that it's more concise and cleaner. Till next time.

AMD and Oracle to collaborate on Heterogenous Computing in Java

In August John Coomes from Oracle made a proposal to add GPU support to Java. One month later, on Sep 10, he proposed the creation of a new project called Sumatra to continue with this endeavour. On Sep 24 this project was approved by a 100% vote in favour. During the recent JavaOne 2012 AMD officially announced its participation in OpenJDK Project Sumatra in collaboration with Oracle and OpenJDK to bring heterogenous computing to Java for server and cloud environments. The Inquirer also reports on this subject.

This is very exciting news indeed. Although there are already two libraries for GPU programming in Java – namely rootbeer and aparapi, having GPU support built in to the Java language, the Java API and most importantly the JVM will provide an alternative more compelling than the use of any external library. And to be quite frank there could not be a collaborator than AMD given their vast contribution to date to OpenCL and OpenCL development tools. And unlike Nvidia, they are wholly committed to OpenCL and not working on their own proprietary alternative.

Although it’ll be a while before this project sees any substantial contribution I cannot wait to see this take form over the next year or two. OpenCL and, in general, the GPU programming paradigm is hard; very hard; and even more importantly porting existing code is even harder; and if anyone can make this domain accessible to the mainstream it’s Java. Once Sumatra is ready hopefully we won’t have to write OpenCL anymore. We’ll be able to write normal Java, compile it and at either compile time or runtime the byte code will get translated into OpenCL and compiled. At execution time we won’t have to worry about what hardware we’re running because with any luck it’ll be write once run anywhere!

Java Lambda FAQ

I came across Maurice Naftalin’s Lambda FAQ recently – an accessible introduction to lambda expressions in Java for the layman who’d rather not read official proposals. Lambdas will make Java a more terse and a more powerful language. With any luck the SDK libraries will leverage them as will the libraries of Doug Lea and Google. Though it remains to be seen what happens to the readability and maintainability over time of code written with lambdas. But overall I’m very excited about this feature indeed. The only thing I’m less than enthusiastic about is the ‘->’ syntax which is reminiscent of PHP and C++ pointers and these are not pleasant memories. I can’t really think what a good alternative may be; perhaps something colon based – but I really hope they do come up with one.