Tiling has proven to be an effective mechanism to develop high performance implementations of algorithms. Tiling can be used to organize computations so that communication costs in parallel programs are reduced and locality in sequential codes or sequential components of parallel programs is enhanced.In this paper, a data type -Hierarchically Tiled Arrays or HTAs -that facilitates the direct manipulation of tiles is introduced. HTA operations are overloaded array operations. We argue that the implementation of HTAs in sequential OO languages transforms these languages into powerful tools for the development of high-performance parallel codes and codes with high degree of locality. To support this claim, we discuss our experiences with the implementation of HTAs for MATLAB and C++ and the rewriting of the NAS benchmarks and a few other programs into HTA-based parallel form.

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Programming for large-scale, multicore-based architectures requires adequate tools that offer ease of\ud programming and do not hinder application performance. StarSs is a family of parallel programming models\ud based on automatic function-level parallelism that targets productivity. StarSs deploys a data-flow model: it\ud analyzes dependencies between tasks and manages their execution, exploiting their concurrency as much\ud as possible.\ud This paper introduces Cluster Superscalar (ClusterSs), a new StarSs member designed to execute on\ud clusters of SMPs (Symmetric Multiprocessors). ClusterSs tasks are asynchronously created and assigned\ud to the available resources with the support of the IBM APGAS runtime, which provides an efficient and\ud portable communication layer based on one-sided communication.\ud We present the design of ClusterSs on top of APGAS, as well as the programming model and\ud execution runtime for Java applications. Finally, we evaluate the productivity of ClusterSs, both in terms\ud of programmability and performance and compare it to that of the IBM X10 languagePeer ReviewedPostprint (published version

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 TM toolbox, overloading conventional operators and array functions such that HTA operations appear to the programmer as extensions of MATLAB TM . 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.

This paper describes our experience with MaJIC, a just-intime compiler for MATLAB. In the recent past, several compiler projects claimed large performance improvements when processing MATLAB code. Most of these projects are static compilers suited for batch processing; MaJIC is a just-in-time compiler. The compilation process is transparent to the user. This impacts the modus operandi of the compiler, resulting in a few interesting analysis techniques. Our experiments with MaJIC indicate large speedups when compared to the interpreter, and reasonable performance when compared to static compilers.