Hahn Kim

Multicore software technologies

Multicore architectures require parallel computation and explicit management of the memory hierarchy, both of which add programming complexity and are unfamiliar to most programmers. While MPI and OpenMP still have a place in the multicore world, the learning curves are simply too steep for most programmers. New technologies are needed to make multicore processors accessible to a larger community. The signal and image processing community stands to benefit immensely from such technologies. This article provides a survey of new software technologies that hide the complexity of multicore architectures, allowing programmers to focus on algorithms instead of architectures.

Technology Requirements for Supporting On-Demand Interactive Grid Computing

It is increasingly being recognized that a large pool of High Performance Computing (HPC) users requires interactive, on-demand access to HPC resources. How to provide these resources is a significant technical challenge that can be addressed from two directions. The first approach is to adapt existing batch queue based HPC systems to make them more interactive. The second approach is to start with existing interactive desktop environments (e.g., MATLAB) and design a system from the ground up that allows interactive parallel computing. The Lincoln Laboratory Grid (LLGrid) project has taken the latter approach. The LLGrid system has been operational for over a year with a few hundred processors and roughly 70 users, having run over 13,000 interactive jobs and consumed approximately 10,000 processor days of computation. This paper compares the on-demand and interactive computing features of four prominent batch queuing systems: openPBS, Sun GridEngine, Condor, and LSF. It goes on to briefly describe the LLGrid system, and how interactive, on-demand computing was achieved on it by binding to a resource management system. Finally, usage characteristics of the LLGrid system are discussed.

pMATLAB: Parallel MATLAB Library for Signal Processing Applications

MATLAB® is one of the most commonly used languages for scientific computing with approximately one million users worldwide. At MIT Lincoln Laboratory, MATLAB is used by technical staff to develop sensor processing algorithms. MATLABs popularity is based on availability of high-level abstractions leading to reduced code development time. Due to the compute intensive nature of scientific computing, these applications often require long running times and would benefit greatly from increased performance offered by parallel computing. pMatlab (www.ll.mit.edu/pMatlab) implements partitioned global address space (PGAS) support via standard operator overloading techniques. The core data structures in pMatlab are distributed arrays and maps, which simplify parallel programming by removing the need for explicit message passing. This paper presents the pMatlab design and results for the HPC Challenge benchmark suite. Additionally, two case studies of pMatlab use are described.

High-Productivity Software Development with pMatlab

In this paper, we explore the ease of tackling a communication-intensive parallel computing task - namely, the 2D fast Fourier transform (FFT). We start with a simple serial Matlab code, explore in detail a ID parallel FFT, and illustrate how it can be extended to multidimensional FFTs.