Using the High Productivity Language Chapel to Target GPGPU Architectures

It has been widely shown that GPGPU architectures offer large performance gains compared to their traditional CPU counterparts for many applications. The downside to these architectures is that the current programming models present numerous challenges to the programmer: lower-level languages, explicit data movement, loss of portability, and challenges in performance optimization. In this paper, we present novel methods and compiler transformations that increase productivity by enabling users to easily program GPGPU architectures using the high productivity programming language Chapel. Rather than resorting to different parallel libraries or annotations for a given parallel platform, we leverage a language that has been designed from first principles to address the challenge of programming for parallelism and locality. This also has the advantage of being portable across distinct classes of parallel architectures, including desktop multicores, distributed memory clusters, large-scale shared memory, and now CPU-GPU hybrids. We present experimental results from the Parboil benchmark suite which demonstrate that codes written in Chapel achieve performance comparable to the original versions implemented in CUDA.NSF CCF 0702260Cray Inc. Cray-SRA-2010-016962010-2011 Nvidia Research Fellowshipunpublishednot peer reviewe

The Hierarchically Tiled Arrays Programming Approach

In this paper, we show our initial experience with a class of objects, called Hierarchically Tiled Arrays (HTAs), that encapsulate parallelism. HTAs allow the construction of single-threaded parallel programs where a master process distributes tasks to be executed by a collection of servers holding the components (tiles) of the HTAs. The tiled and recursive nature of HTAs facilitates the adaptation of the programs that use them to varying machine configurations, and eases the mapping of data and tasks to parallel computers with a hierarchical organization. We have implemented HTAs as a MATLAB toolbox, overloading conventional operators and array functions such that HTA operations appear to the programmer as extensions . Our experiments show that the resulting environment is ideal for the prototyping of parallel algorithms and greatly improves the ease of development of parallel programs while providing reasonable performance. Categories and Subject Descriptors D.1.3 [Programming Techniques]: Concurrent Programming; D.3.3 [Programming Languages]: Language Constructs and Features General Terms Languages Keywords Parallel languages This work has been supported in part by the Defense Advanced Research Project Agency under contract NBCH30390004. This work is not necessarily representative of the positions or policies of the U.S. Army or Government. It has also been supported in part by the Ministry of Science and Technology of Spain under contract TIC20013694 -C02-02, and by the Xunta de Galicia under contract PGIDIT03-TIC10502PR