International Workshop on OpenCL 

About: International Workshop on OpenCL is an academic conference. The conference publishes majorly in the area(s): Computer science & CUDA. Over the lifetime, 224 publications have been published by the conference receiving 1153 citations.

Gzip on a chip: high performance lossless data compression on FPGAs using OpenCL

Abstract: Hardware implementation of lossless data compression is important for optimizing the capacity/cost/power of storage devices in data centers, as well as communication channels in high-speed networks. In this work we use the Open Computing Language (OpenCL) to implement high-speed data compression (Gzip) on a field-programmable gate-arrays (FPGA). We show how we make use of a heavily-pipelined custom hardware implementation to achieve the high throughput of ~3 GB/s with more than 2x compression ratio over standard compression benchmarks. When compared against a highly-tuned CPU implementation, the performance-per-watt of our OpenCL FPGA implementation is 12x better and compression ratio is on-par. Additionally, we compare our implementation to a hand-coded commercial implementation of Gzip to quantify the gap between a high-level language like OpenCL, and a hardware description language like Verilog. OpenCL performance is 5.3% lower than Verilog, and area is 2% more logic and 25% more of the FPGA's available memory resources but the productivity gains are significant.

CLBlast: A Tuned OpenCL BLAS Library

Abstract: This work introduces CLBlast, an open-source BLAS library providing optimized OpenCL routines to accelerate dense linear algebra for a wide variety of devices. It is targeted at machine learning and HPC applications and thus provides a fast matrix-multiplication routine (GEMM) to accelerate the core of many applications (e.g. deep learning, iterative solvers, astrophysics, computational fluid dynamics, quantum chemistry). CLBlast has five main advantages over other OpenCL BLAS libraries: 1) it is optimized for and tested on a large variety of OpenCL devices including less commonly used devices such as embedded and low-power GPUs, 2) it can be explicitly tuned for specific problem-sizes on specific hardware platforms, 3) it can perform operations in half-precision floating-point FP16 saving bandwidth, time and energy, 4) it has an optional CUDA back-end, 5) and it can combine multiple operations in a single batched routine, accelerating smaller problems significantly. This paper describes the library and demonstrates the advantages of CLBlast experimentally for different use-cases on a wide variety of OpenCL hardware.

Khronos SYCL for OpenCL: a tutorial

Abstract: SYCL ([sikə l] as in sickle) is a royalty-free, cross-platform C++ abstraction layer that builds on the underlying concepts, portability and efficiency of OpenCL, while adding the ease-of-use and flexibility of modern C++11. For example, SYCL enables single source development where C++ template functions can contain both host and device code to construct complex algorithms that use OpenCL acceleration, and then re-use them throughout their source code on different types of data.In this tutorial we will introduce the concepts behind OpenCL SYCL, present an implementation of SYCL targeting OpenCL devices with SPIR based on Clang/LLVM and an open source CPU-only implementation based on C++1z, Boost and OpenMP.Attendees of the last session are encouraged to install the open-source CPU-only implementation of SYCL and code along on laptop/tablet.