We are proud to announce another exciting release of the ArrayFire library. This version fixes a critical performance regression in the ArrayFire just in time kernel generation code. We discovered the regression late in the release window for the v3.6.3 release and we couldn’t address it in the previous version so this version only consists of 2 commits. Please go check out the https://arrayfire.com/download page for the latest installers.
ArrayFire v3.6.2 Release
We are excited to announce ArrayFire v3.6.2! In this release we have fixed a number of bugs, improved documentation, and added a few features while improving performance as always. We highlight some of the exciting changes below. New features and improvements Several features added in v3.6.2 are concerned with batching and broadcasting. In v3.6 we introduced batched matmul() allowing you to multiply several matrices at the same time. You could only batch arrays that were the same shape and size on the left hand side and the right hand size. In cases when you wanted to multiply one matrix with multiple matrices, you had to tile the inputs so that they were the same shape before you performed the multiplication. …
ArrayFire v3.5.1 Release
We are excited to announce ArrayFire v3.5.1! This release focuses on fixing bugs and improving performance. Here are the improvements we think are most important: Performance improvements We’ve improved element-wise operation performance for the CPU backend. The af::regions() function has been modified to leverage texture memory, improving its performance. Our JIT engine has been further optimized to boost performance. Bug fixes We’ve squashed a long standing bug in the CUDA backend responsible for breaking whenever the second, third, or fourth dimensions were large enough to exceed limits imposed by the CUDA runtime. The previous implementation of af::mean() suffered from overflows when the summation of the values lied outside the range of the backing data type. New kernels for each of …
ArrayFire v3.5 Official Release
Today we are pleased to announce the release of ArrayFire v3.5, our open source library of parallel computing functions supporting CUDA, OpenCL, and CPU devices. This new version of ArrayFire improves features and performance for applications in machine learning, computer vision, signal processing, statistics, finance, and more. This release focuses on thread-safety, support for simple sparse-dense arithmetic operations, canny edge detector function, and a genetic algorithm example. A complete list of ArrayFire v3.5 updates and new features are found in the product Release Notes. Thread Safety ArrayFire now supports threading programming models. This is not intended to improve the performance since most of the parallelism is happening on the device, but it does allow you to use multiple devices in …
AccelerEyes Celebrates 5 Years with New Product Releases
AccelerEyes just marked its 5th year in business. What better way to celebrate than by releasing new products! We are pleased to present ArrayFire v1.2 and Jacket v2.2 for NVIDIA CUDA-based GPUs. These new products support the latest Kepler architecture and include an array of new features and performance boosts, especially for image processing functions. Learn more in the ArrayFire release notes and Jacket release notes. AccelerEyes started up in 2007 with the mission to make productive performance accessible to engineers, scientists, and financial analysts. Our core leadership has been to provide great libraries that are easy-to-use and faster than alternative approaches. The coolest part about working at AccelerEyes is getting to play a part in the awesome projects of our …