We are pleased to announce a new release of the ArrayFire library, v3.9.0. This release makes it easier than ever to target new devices without sacrificing performance. This post describes four of these new features, including: oneAPI Backend This release is the first time since v3.0 that introduces a new backend. The new backend is built with the oneAPI specification on top of the SYCL language. oneAPI is an open specification providing a full framework for high-performance computing applications without vendor lock-in. While this has been possible with OpenCL, the oneAPI specification includes libraries like BLAS and FFT significantly reducing the burden on the developers to maintain math functions and increasing the performance of these common operations. Here is a …
Cycling through SYCL
We recently gave an overview of recent history in the technical computing hardware market. In it, we mention the energy at Intel right now. The weight of Intel is behind the SYCL standard through its new software approach, oneAPI. SYCL is a cross-platform API that targets heterogeneous hardware, similar to OpenCL and CUDA. The SYCL standard was first introduced by Codeplay and is now being managed by the Khronos group. It allows single-source compilation in C++ to target multiple devices on a system, rather than using C++ for the host and domain specific kernel languages for the device. Furthermore, SYCL is fully C++ 17 standards compliant. You don’t have any extensions to the language that would prevent any standards compliant …
Benchmarking parallel vector libraries
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 …
Feature detection and tracking using ArrayFire
A few weeks ago we added some computer vision functionality to our open source ArrayFire GPU computing library. Specifically, we implemented the FAST feature extractor, BRIEF feature point descriptor, ORB multi-resolution scale invariant feature extractor, and a Hamming distance function. When combined, these functions enable you to find features in videos (or images) and track them between successive frames.
Using zero-copy buffers on integrated GPUs
One of the most powerful aspects of parallel program on integrated GPUs is taking advantage of shared memory and caches. The best example of this is sharing common data between the CPU and GPU via. zero-copy buffers. This technique permits your program to avoid the O(N) cost of copying data to/from the GPU. This feature is particularly useful for applications that deal with real-time data streams, like video processing.
Demystifying PTX Code
In my recent post, I showed how to generate PTX files from both CUDA and OpenCL kernels. In this post I will address the issue of how a PTX file look, and more importantly, how to understand all those complicated instructions in a PTX files. In this post I will use the same vector addition kernel from the the previous post previous post (the complete code can be found here). For this post, I will focus on OpenCL PTX file. In a future post I will discuss the differences between PTX files of OpenCL and CUDA code. Let’s start by looking at the complete PTX code: // // Generated by NVIDIA NVVM Compiler // Compiler built on Sun May 18 …
Cross Compile to Windows From Linux
Why did I not know about this? It’s like I just discovered the screw driver! On Debian and variants (from tinc’s windows cross-compilation page), sudo apt-get install mingw-w64 # C i686-w64-mingw32-gcc hello.c -o hello32.exe # 32-bit x86_64-w64-mingw32-gcc hello.c -o hello64.exe # 64-bit # C++ i686-w64-mingw32-g++ hello.cc -o hello32.exe # 32-bit x86_64-w64-mingw32-g++ hello.cc -o hello64.exe # 64-bit Granted, this isn’t a silver bullet, but rather a quick way to get a Windows build of platform independent code that you might already have running in Linux. I’ve found that this approach makes it easy to get binaries out the door in a hurry when it’s hard to get a project building with Visual Studio or even on the Windows platform itself (due …
Image editing using ArrayFire: Part 3
Today, we will be doing the third post in our series Image editing using ArrayFire. References to old posts are available below. * Part 1 * Part 2 In this post, we will be looking at the following operations. Image Histogram Simple Binary Theshold Otsu Threshold Iterative Threshold Adaptive Binary Threshold Emboss Filter Today’s post will be mostly dominated by different types of threshold operations we can achieve using ArrayFire. Image Histogram We have a built-in function in ArrayFire that creates a histogram. The input image was converted to gray scale before histogram calculation as our histogram implementation works for vector and 2D matrices only. In case, you need histogram for all three channels of a color image, you can …
Quest for the Smallest OpenCL Program
I have heard many complaints about the verbosity of the OpenCL API. This claim is not unwarranted. The verbosity is due to the low-level nature of OpenCL. It is written in the C programming language; the lingua franca of programming languages. While this allows you to run an OpenCL program on virtually any platform, it has some disadvantages. A typical OpenCL program must: Query for the platform Get the device IDs from the platform Create a context from a set of device IDs Create a command queue from the context Create buffer objects for your data Transfer the data to the buffer Create and build a program from source Extract the kernels Launch the kernels Transfer the data to the host …