Modular Array-based GPU Computing in a Dynamically-typed Language
Nowadays, GPU accelerators are widely used in areas with large data-parallel computations such as scientific computations or neural networks. Programmers can either write code in low-level CUDA/OpenCL code or use a GPU extension for a high-level programming language for better productivity. Most extensions focus on statically-typed languages, but many programmers prefer dynamically-typed languages due to their simplicity and flexibility.
This paper shows how programmers can write high-level modular code in Ikra, a Ruby extension for array-based GPU computing. Programmers can compose GPU programs of multiple reusable parallel sections, which are subsequently fused into a small number of GPU kernels. We propose a seamless syntax for separating code regions that extensively use dynamic language features from those that are compiled for efficent execution. Moreover, we propose symbolic execution and a program analysis for kernel fusion to achieve performance that is close to hand-written CUDA code.
Sun 18 Jun Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
|16:00 - 16:30|
|Efficient Array Slicing on the Intel Xeon Phi Coprocessor|
Benjamin AndreassenNorwegian University of Science and Technology, Jan ChristianNorwegian University of Science and Technology, Lasse NatvigNorwegian University of Science and TechnologyDOI File Attached
|16:30 - 17:00|
|Modular Array-based GPU Computing in a Dynamically-typed Language|
Matthias SpringerTokyo Institute of Technology, Peter WauligmannTokyo Institute of Technology, Hidehiko MasuharaTokyo Institute of TechnologyDOI File Attached
|17:00 - 17:30|
|HPTT: A High-Performance Tensor Transposition C++ Library|
ARRAYDOI File Attached