Yes, you read that right! ArrayFire is open source—it’s all there and it’s all free. This is big, and you and the rest of the parallel computing community are going to love it! You can download our pre-compiled binary installers which are optimized for a wide variety of systems or you can get a copy of the ArrayFire source code from our GitHub page. ArrayFire is being released under the BSD 3-Clause License, which will enable unencumbered deployment and portability of ArrayFire for commercial use. So go check it out! We welcome your feedback and look forward to your future contributions to ArrayFire. The move to open source isn't our only news—we've also made ArrayFire better than ever. Check out our recent ...
Last month, I posted two articles describing computing trends and why heterogeneous computing will be a significant force in computing for the next decade. Today, I continue that series with an article describing the biggest challenge to continued increases in computing performance - parallel software development. Biggest Challenge As I described previously, in order to use an accelerator, software changes must be made. Regular x86-based compilers cannot compile code to run on accelerators without these needed changes. The amount of software change required varies depending upon the availability of and reliance upon software tools that increase performance and productivity. There are four possible approaches to take advantage of accelerators in heterogeneous computing environments: do-it-yourself, use compilers, use libraries, or use ...
Ten days ago, I posted an article on CPU Processing Trends for Dummies. Today, I continue that series with an article describing the latest major trend in computing, namely Heterogeneous Computing. The Point The point of these articles is to paint the high-level picture for trends in computer processing. I hope this bigger picture will help summarize things for those that do not breathe computer processors and technical software on a daily basis. Over the last 20 years, big gains in computer processing have been defined by increases in CPU clock speeds, then by increases in the number of CPU cores. The next 10+ years will be defined by heterogeneous computing. Heterogeneous Computing So let's start with a definition: Heterogeneous ...
Over the years at AccelerEyes, it has been surprising to me how many people miss a big picture understanding of the trends affecting the computing industry. To help, I'm going to post a few articles with high-level explanations. I'm going to do so in a hand-wavy manner. I look forward in advance to the lively comments on my mistakes. But, in general, I think these posts will be a fairly accurate view of the important trends. Today, I'll start by talking about CPU processing trends. Let's start with something we all know: CPUs are central processing units and are the main processor in the computer. You probably had to label the CPU on a diagram at some point in grade school, ...
In order to get the best performance from your CUDA or OpenCL code, it is helpful to keep in mind some useful tips for optimizing performance. Note: By "accelerator" we refer to GPUs, APUs, co-processors, FPGAs, and any devices capable of running CUDA or OpenCL. Vectorized Code: Accelerators perform best with vectorized code because the computations map naturally onto arithmetic cores of the hardware. ArrayFire functions are inherently vectorized, so if you are using ArrayFire, you are writing vectorized code. Memory Transfers: Avoid excessive memory transfers. Each casting operation to and from the accelerator moves data back and forth between CPU memory and accelerator memory. ArrayFire makes many automatic optimizations to minimize these memory transfers by only transferring data when ...
A free, fast, and simple GPU library for CUDA and OpenCL devices. AccelerEyes announces the launch of ArrayFire, a freely-available GPU software library supporting CUDA and OpenCL devices. ArrayFire supports C, C++, Fortran, and Python languages on AMD, Intel, and NVIDIA hardware. Learn more by visiting the ArrayFire product page. "ArrayFire is our best software yet and anyone considering GPU computing can benefit," says James Malcolm, VP Engineering at AccelerEyes. "It is fast, simple, GPU-vendor neutral, full of functions, and free for most users." Thousands of paying customers currently enjoy AccelerEyes’ GPU software products. With ArrayFire, everyone developing software for GPUs has an opportunity to enjoy these benefits without the upfront expense of a developer license. Reasons to use ArrayFire: ...
Whether you are a new Jacket programmer or a GPU maestro, you are bound to speed-test Jacket at some point. There are many factors to keep in mind while benchmarking Jacket code - a simple tic-func()-toc won't do. For example, this is some typical benchmarking code:
% warm up
x = rand(n,'single');
x = grand(n, 'single');
% CPU timing
for r = 1:reps
x = rand(n,'single');
cpu_time = toc;
% GPU timing
for r = 1:reps
x = grand(n,'single');
gsync, gpu_time = toc
With Jacket 1.7, this entire code chunk is now replaced by two lines:
cpu_time = timeit(@() rand(n,'single'));
gpu_time = timeit(@() grand(n,'single'));
One Jacket programmer recently emailed the following to us: Our chief scientists asked me a question that I'd like to pass on to you. I think I know the answer, but you guys can be much more definitive than I can. He recently read about people achieving ~10x speedups by converting parts of their code to MEX files. He was wondering how much of the observed speedup is due to that MEX and how much is due to CUDA and the GPU. Two Questions You Should Ask Yourself When contemplating an effort to optimize a piece of code, it is important to unravel the effort into two separate questions. Both need to be addressed to improve performance: How well-written is ...
Amazon recently added GPUs to their Elastic Compute Cloud. We decided to throw LIBJACKET into this GPU cloud to see how it would fare. The $2/hr pay-on-demand pricing is a great option for many Jacket programmers. This post is full of screenshots detailing the steps we took to get going with GPU computing in Amazon's cloud: Sign up with Amazon EC2 Launch a GPU instance Login to the instance using ssh Setup the environment Download, build, and test LIBJACKET! Everything in this post applies equally well to running Jacket for MATLAB® on EC2. Simply install MATLAB + Jacket in your Amazon GPU instance and start working over ssh.
We just returned from SC'10, the biggest supercomputing show of the year. At the show, we demoed Jacket driving computer vision demos on an 8-GPU Colfax CXT8000 system... pure eye candy! We had CPU and GPU versions of the demos running on 8 different monitors, each attached to the 8 Tesla C2050 GPUs in the system. Input data for the various demos was sourced from 3 webcams and 2 Blu-ray video inputs. Checkout the demo details, below: Demo 1 Sobel edge detection with image dilation and interpolation overlaid on Blu-ray video in realtime. Demo 2 Feature detection on a 4-level pyramid of 640x480 realtime webcam video. Demo 3 Gradient descent feature tracking , a stripped down version of KLT, tracking ...
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