Sabri Pllana

Jesper Larsson Tr&#xe4;ff

Introduction to the Scientific Programming Special Issue: Software Development for Multi-core Computing Systems

Lee S. Brownston

Book Review

In this work we present a runtime threading system which provides an efficient substrate for fine-grain parallelism, suitable for deployment in multicore platforms. Its architecture encompasses a number of optimizations that make it particularly effective in managing a large number of threads and with low overheads. The runtime system has been integrated into an OpenMP implementation to allow for transparent usage under a high level programming paradigm. We evaluate our implementation on two multicore systems using synthetic microbenchmarks and a real-time face detection application.

P.E. Hadjidoukas

G.Ch. Philos

V.V. Dimakopoulos

Exploiting Fine-Grain Thread Parallelism on Multicore Architectures

Reviewers for Scientific Programming Special Issue on Software Development for Multi-core Computing Systems

Hybrid parallel multicore architectures based on graphics processing units (GPUs) can provide tremendous computing power. Current NVIDIA and AMD Graphics Product Group hardware display a peak performance of hundreds of gigaflops. However, exploiting GPUs from existing applications is a difficult task that requires non-portable rewriting of the code. In this paper, we present HMPP, a Heterogeneous Multicore Parallel Programming workbench with compilers, developed by CAPS entreprise, that allows the integration of heterogeneous hardware accelerators in a unintrusive manner while preserving the legacy code.

Francois Bodin

Stephane Bihan

Heterogeneous Multicore Parallel Programming for Graphics Processing Units

Dan Nagle

One of the challenges to achieving good performance on multicore architectures is the effective utilization of the underlying memory hierarchy. While this is an issue for single-core architectures, it is a critical problem for multicore chips. In this paper, we formulate the unified multicore model (UMM) to help understand the fundamental limits on cache performance on these architectures. The UMM seamlessly handles different types of multiple-core processors with varying degrees of cache sharing at different levels. We demonstrate that our model can be used to study a variety of multicore architectures on a variety of applications. In particular, we use it to analyze an option pricing problem using the trinomial model and develop an algorithm for it that has near-optimal memory traffic between cache levels. We have implemented the algorithm on a two Quad-Core Intel Xeon 5310 1.6 GHz processors (8 cores). It achieves a peak performance of 19.5 GFLOPs, which is 38&#x25; of the theoretical peak of the multicore system. We demonstrate that our algorithm outperforms compiler-optimized and auto-parallelized code by a factor of up to 7.5.

John E. Savage

Mohammad Zubair

Evaluating Multicore Algorithms on the Unified Memory Model

For mobile multiprocessor applications, achieving high performance with low energy consumption is a challenging task. In order to help programmers to meet these design requirements, system development tools play an important role. In this paper, we describe one such development tool, ePRO-MP, which profiles and optimizes both performance and energy consumption of multi-threaded applications running on top of Linux for ARM11 MPCore-based embedded systems. One of the key features of ePRO-MP is that it can accurately estimate the energy consumption of multi-threaded applications without requiring a power measurement equipment, using a regression-based energy model. We also describe another key benefit of ePRO-MP, an automatic optimization function, using two example problems. Using the automatic optimization function, ePRO-MP can achieve high performance and low power consumption without programmer intervention. Our experimental results show that ePRO-MP can improve the performance and energy consumption by 6.1&#x25; and 4.1&#x25;, respectively, over a baseline version for the co-running applications optimization example. For the producer-consumer application optimization example, ePRO-MP improves the performance and energy consumption by 60.5&#x25; and 43.3&#x25;, respectively over a baseline version.

Wonil Choi

Hyunhee Kim

Wook Song

Jiseok Song

Jihong Kim

ePRO-MP: A Tool for Profiling and Optimizing Energy and Performance of Mobile Multiprocessor Applications

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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

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Chief Editor&nbsp;Professor Tramontana&nbsp;is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.

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Scientific Programming | Hindawi

Scientific Programming

This Issue is now open for submissions.
Papers are published upon acceptance, regardless of the Special Issue publication date.

Scientific Programming

Software Development for Multi-core Computing Systems

Software Development for Multi-core Computing Systems

Articles

Introduction to the Scientific Programming Special Issue: Software Development for Multi-core Computing Systems

Book Review

Exploiting Fine-Grain Thread Parallelism on Multicore Architectures

Reviewers for Scientific Programming Special Issue on Software Development for Multi-core Computing Systems

Heterogeneous Multicore Parallel Programming for Graphics Processing Units

Book Review

Evaluating Multicore Algorithms on the Unified Memory Model

ePRO-MP: A Tool for Profiling and Optimizing Energy and Performance of Mobile Multiprocessor Applications