There are many open source libraries that implement parallel versions of the algorithms in the C++ standard template libraries. Inevitably we get asked questions about how ArrayFire compares to the other libraries out in the open. In this post we are going to compare the performance of ArrayFire to that of BoostCompute, HSA-Bolt, Intel TBB and Thrust. The benchmarks include the following commonly used vector algorithms across 3 different architectures. Reductions Scan Transform The following setup has been used for the benchmarking purposes. The code to reproduce the benchmarks is linked at the bottom of the post. The hardware used for the benchmarks is listed below: NVIDIA Tesla K20 AMD FirePro S10000 Intel Xeon E5-2560v2 Background ArrayFire ArrayFire provides high …
Machine Learning with ArrayFire
In case you missed it, we recently held a webinar on the ArrayFire GPU Computing Library and its applications to Machine Learning on June 15. This webinar was part of a free series of webinars that help you learn about ArrayFire and Jacket (our MATLAB® product). Anyone can attend these webinars, for they are absolutely free and open for anyone to attend and interact with AccelerEyes engineers. Learn more at http://www.accelereyes.com/webinars. Chris, a Software Engineer at AccelerEyes, explained ArrayFire’s position in the GPU computing world, and presented benchmarks where ArrayFire beats GPU libraries such as Thrust in many critical applications. He also mentioned that ArrayFire could be used either standalone, or in combination with other options for GPU computing such …