As we engage with GPU programmers everywhere, we find ourselves each day assisting others in configuring NVIDIA drivers. This post is aimed at Ubuntu users.
ArrayFire Examples and Benchmarks Whitepaper
What do you get when you offer the world’s most comprehensive GPU library available for free? Excited users who go the extra mile and give back to the community. Andrzej Chrzȩszczyk from Jan Kochanowski University recently wrote an awesome whitepaper, entitled “Matrix Computations on the GPU with ArrayFire for Python and C/C++.” The whitepaper contains many GPU computing tutorial examples as well as performance timings for each example. Andrzej notes, “The purpose of this document is to make the first steps in using modern graphics cards to general purpose computations simpler.” This document is especially beneficial for programmers looking to accelerate Python or C/C++ codes. We thank Andrzej this fine contribution to the ArrayFire community. His documentation on ArrayFire will be beneficial to all …
ArrayFire for Defense and Intelligence Applications
In case you missed it, we recently held a webinar on the ArrayFire GPU Computing Library and its applications to Defense and Intelligence functions. Defense projects often have hard deadlines and definite speed targets, and ArrayFire is a fast and easy-to-use choice for these applications. This webinar was part of an ongoing series of webinars that will help you learn more about the many applications of Jacket and ArrayFire, while interacting with AccelerEyes GPU computing experts. John Melonakos, our CEO, introduced ArrayFire and talked about some exciting recent customer successes in the field of defense. He then ran through the mechanics of compiling and running code on a machine with 2 Quadro 6000 GPUs, and talked about customer success stories. …
ArrayFire for Financial Computing Applications
In case you missed it, we recently held a webinar on how to accelerate financial computing applications using Jacket. The performance advantages brought to financial computing algorithms through Jacket and GPUs represents the best way to accelerate MATLAB® code. This webinar was part of an ongoing series of webinars that will help you learn more about the many applications of Jacket and ArrayFire, while interacting with AccelerEyes GPU computing experts. Scott Blakeslee, our Director of Business Development, introduced Jacket and talked about some exciting recent customer successes in the field of financial computing. Gallagher Pryor, CTO of AccelerEyes, then demoed some financial code speedups on one of our office machines. The major takeaway from the webinar video was that Jacket is …
ArrayFire Pro : Features and Scalability
ArrayFire is a fast GPU library that off-loads compute intensive tasks onto many-core GPUs, thereby reducing application runtime and accelerating it many times. ArrayFire is built on top of NVIDIA CUDA software stack which is currently the best and most stable GPU Software Development Kit available for GPU-based computing. ArrayFire comes with a huge set of functions that span across various domains like image processing, signal processing, financial modeling, applications requiring graphics support. ArrayFire has an array based notation (supports N-dimensional arrays) and allows sub-referencing and assignment into these multi-dimensional arrays. The following code snippet shows how you can index into array objects. // Generate a 3×3 array of random numbers on the GPU array A = randu(3,3); array a1 …
OpenCL vs CUDA Comparisons
In case you missed it, we recently held an ArrayFire Webinar, focused on exploring the tradeoffs of OpenCL vs CUDA. This webinar is part of an ongoing series of webinars held each month to present new GPU software topics as well as programming techniques with Jacket and ArrayFire. For those of you who missed it, we provide a recap here. Lots of questions were fielded by our team, so it’s a must-watch. We hope to see you at the next one! Recap Download the slides. Here is a transcript of the content portion of the webinar: AccelerEyes is pleased to present today’s ArrayFire webinar looking at OpenCL and CUDA Trade-offs and Comparisons. Everyday, we interact with many programmers in various stages of GPU …
ArrayFire for Medical Image Segmentation
In case you missed it, we recently held a webinar on how to accelerate common medical imaging applications using an easy, powerful programming library with Jacket for MATLAB®. This webinar was part of an ongoing series of webinars that will help you learn more about the many applications of Jacket and ArrayFire, while interacting with AccelerEyes GPU computing experts. Gallagher Pryor, CTO of AccelerEyes, used the Bayesian Image Segmentation algorithm as a simple use-case to show how easy it is to convert CPU code to GPU code with Jacket (only 4 lines of CPU code needed to be changed!). For those of you who missed it, we uploaded the webinar on Youtube. We hope to see you at the next one!
Jacket over Remote Desktop for Tesla and Quadro GPUs
We recently reported that Jacket could be used over Windows Remote Desktop connections as long as you had an NVIDIA Tesla device in TCC mode. With the latest NVIDIA driver updates, Tesla and Quadro devices can be put into TCC mode, making it possible to use Jacket over Remote Desktop with both Tesla and Quadro devices. We have tested this out with the NVIDIA Quadro 4000 as well as Quadro 6000 GPUs. The system had a Tesla C2050 connected to the display, and the Quadro in TCC mode. Here’s the ginfo output: >> ginfo Jacket v2.0 (build 80c7ba4) by AccelerEyes (64-bit Windows) License Type: Designated Computer ([JACKET_ROOT]jacketenginejlicense.dat) Addons: MGL4, JMC, SDK, DLA, SLA CUDA toolkit 4.0, driver 285.62 GPU1 Quadro …
Filtering Benchmarks – OpenCV GPU vs LibJacket
OpenCV is one of the most popular computer vision toolkits, and over the last year they’ve been integrating more GPU processing into the core. One of the most common image processing tasks is convolution. Since LibJacket and OpenCV both support this, one of my coworkers rolled up his sleeves and benchmarked the latest versions from both libraries: OpenCV/CPU, OpenCV/GPU, LibJacket. Jump over to his personal website for the full benchmark results and source code. From the graphs, the GPU implementations from OpenCV and LibJacket both easily outperform the default CPU version in OpenCV, but notice that LibJacket pushes performance even further and dominates OpenCV’s GPU implementation, especially when using separable filters. We’ve worked really hard the last few years to …
Optimization methods for deep learning
Researchers at SAIL (Stanford Artificial Intelligence Laboratory), have done it again. They have successfully used Jacket to speed up the training part of Deep Learning algorithms. In their paper titled “On Optimization Methods for Deep Learning”, they experiment with some of the well known training algorithms and demostrate their scalability across parallel architectures (GPUs as well as multi-machine networks). The algorithms include SGDs (Stochastic Gradient Descent) L-BFGS (Limited BFGS used for solving non-linear problems), CG (Conjugate Gradient). While SGDs are easy to implement, they require manual tuning. Add to that their sequential nature, they are hard to tune, scale and parallelize making them difficult to use with Deep Learning algorithms. L-BFGS and CG algorithms can be harder to implement and …