Michael Gschwind

Fred Gustavson

Jan F. Prins

High Performance Computing with the Cell Broadband Engine

The QR factorization is one of the most important operations in dense linear algebra, offering a numerically stable method for solving linear systems of equations including overdetermined and underdetermined systems. Modern implementations of the QR factorization, such as the one in the LAPACK library, suffer from performance limitations due to the use of matrix&#x2013;vector type operations in the phase of panel factorization. These limitations can be remedied by using the idea of updating of QR factorization, rendering an algorithm, which is much more scalable and much more suitable for implementation on a multi-core processor. It is demonstrated how the potential of the cell broadband engine can be utilized to the fullest by employing the new algorithmic approach and successfully exploiting the capabilities of the chip in terms of single instruction multiple data parallelism, instruction level parallelism and thread-level parallelism.

Jakub Kurzak

Jack Dongarra

QR Factorization for the Cell Broadband Engine

The enormous growth of biological sequence databases has caused bioinformatics to be rapidly moving towards a data-intensive, computational science. As a result, the computational power needed by bioinformatics applications is growing rapidly as well. The recent emergence of low cost parallel multicore accelerator technologies has made it possible to reduce execution times of many bioinformatics applications. In this paper, we demonstrate how the Cell Broadband Engine can be used as a computational platform to accelerate two approaches for protein sequence database scanning: exhaustive and heuristic. We present efficient parallelization techniques for two representative algorithms: the dynamic programming based Smith&#x2013;Waterman algorithm and the popular BLASTP heuristic. Their implementation on a Playstation&#xae;3 leads to significant runtime savings compared to corresponding sequential implementations.

Adrianto Wirawan

Bertil Schmidt

Huiliang Zhang

Chee Keong Kwoh

High Performance Protein Sequence Database Scanning on the Cell Broadband Engine

Sandya S. Mannarswamy

Erratum

The Cell Broadband Engine architecture is a revolutionary processor architecture well suited for many scientific codes. This paper reports on an effort to implement several traditional high-performance scientific computing applications on the Cell Broadband Engine processor, including molecular dynamics, quantum chromodynamics and quantum chemistry codes. The paper discusses data and code restructuring strategies necessary to adapt the applications to the intrinsic properties of the Cell processor and demonstrates performance improvements achieved on the Cell architecture. It concludes with the lessons learned and provides practical recommendations on optimization techniques that are believed to be most appropriate.

Guochun Shi

Volodymyr V. Kindratenko

Ivan S. Ufimtsev

Todd J. Martinez

James C. Phillips

Steven A. Gottlieb

Implementation of Scientific Computing Applications on the Cell Broadband Engine

Lattice Quantum Chromodynamic (QCD) models subatomic interactions based on a four-dimensional discretized space&#x2013;time continuum. The Lattice QCD computation is one of the grand challenges in physics especially when modeling a lattice with small spacing. In this work, we study the implementation of the main kernel routine of Lattice QCD that dominates the execution time on the Cell Broadband Engine. We tackle the problem of efficient SIMD execution and the problem of limited bandwidth for data transfers with the off-chip memory. For efficient SIMD execution, we present runtime data fusion technique that groups data processed similarly at runtime. We also introduce analysis needed to reduce the pressure on the scarce memory bandwidth that limits the performance of this computation. We studied two implementations for the main kernel routine that exhibit different patterns of accessing the memory and thus allowing different sets of optimizations. We show the attributes that make one implementation more favorable in terms of performance. For lattice size that is significantly larger than the local store, our implementation achieves 31.2 GFlops for single precision computations and 16.6 GFlops for double precision computations on the PowerXCell 8i, an order of magnitude better than the performance achieved on most general-purpose processors.

Khaled Z. Ibrahim

Fran&#xe7;ois Bodin

Efficient SIMDization and Data Management of the Lattice QCD Computation on the Cell Broadband Engine

Interactive high quality volume rendering is becoming increasingly more important as the amount of more complex volumetric data steadily grows. While a number of volumetric rendering techniques have been widely used, ray casting has been recognized as an effective approach for generating high quality visualization. However, for most users, the use of ray casting has been limited to datasets that are very small because of its high demands on computational power and memory bandwidth. However the recent introduction of the Cell Broadband Engine (Cell B.E.) processor, which consists of 9 heterogeneous cores designed to handle extremely demanding computations with large streams of data, provides an opportunity to put the ray casting into practical use. In this paper, we introduce an efficient parallel implementation of volume ray casting on the Cell B.E. The implementation is designed to take full advantage of the computational power and memory bandwidth of the Cell B.E. using an intricate orchestration of the ray casting computation on the available heterogeneous resources. Specifically, we introduce streaming model based schemes and techniques to efficiently implement acceleration techniques for ray casting on Cell B.E. In addition to ensuring effective SIMD utilization, our method provides two key benefits: there is no cost for empty space skipping and there is no memory bottleneck on moving volumetric data for processing. Our experimental results show that we can interactively render practical datasets on a single Cell B.E. processor.

Jusub Kim

Joseph JaJa

Streaming Model Based Volume Ray Casting Implementation for Cell Broadband Engine

In this paper we present the design and implementation of the Linpack benchmark for the IBM BladeCenter QS22, which incorporates two IBM PowerXCell 8i1 processors. The PowerXCell 8i is a new implementation of the Cell Broadband Engine&#x2122;2 architecture and contains a set of special-purpose processing cores known as Synergistic Processing Elements (SPEs). The SPEs can be used as computational accelerators to augment the main PowerPC processor. The added computational capability of the SPEs results in a peak double precision floating point capability of 108.8 GFLOPS. We explain how we modified the standard open source implementation of Linpack to accelerate key computational kernels using the SPEs of the PowerXCell 8i processors. We describe in detail the implementation and performance of the computational kernels and also explain how we employed the SPEs for high-speed data movement and reformatting. The result of these modifications is a Linpack benchmark optimized for the IBM PowerXCell 8i processor that achieves 170.7 GFLOPS on a BladeCenter QS22 with 32 GB of DDR2 SDRAM memory. Our implementation of Linpack also supports clusters of QS22s, and was used to achieve a result of 11.1 TFLOPS on a cluster of 84 QS22 blades. We compare our results on a single BladeCenter QS22 with the base Linpack implementation without SPE acceleration to illustrate the benefits of our optimizations.

Michael Kistler

John Gunnels

Daniel Brokenshire

Brad Benton

Programming the Linpack Benchmark for the IBM PowerXCell 8i Processor

Matrix factorization (or often called decomposition) is a frequently used kernel in a large number of applications ranging from linear solvers to data clustering and machine learning. The central contribution of this paper is a thorough performance study of four popular matrix factorization techniques, namely, LU, Cholesky, QR and SVD on the STI Cell broadband engine. The paper explores algorithmic as well as implementation challenges related to the Cell chip-multiprocessor and explains how we achieve near-linear speedup on most of the factorization techniques for a range of matrix sizes. For each of the factorization routines, we identify the bottleneck kernels and explain how we have attempted to resolve the bottleneck and to what extent we have been successful. Our implementations, for the largest data sets that we use, running on a two-node 3.2 GHz Cell BladeCenter (exercising a total of sixteen SPEs), on average, deliver 203.9, 284.6, 81.5, 243.9 and 54.0 GFLOPS for dense LU, dense Cholesky, sparse Cholesky, QR and SVD, respectively. The implementations achieve speedup of 11.2, 12.8, 10.6, 13.0 and 6.2, respectively for dense LU, dense Cholesky, sparse Cholesky, QR and SVD, when running on sixteen SPEs. We discuss the interesting interactions that result from parallelization of the factorization routines on a two-node non-uniform memory access (NUMA) Cell Blade cluster.

B.C. Vishwas

Abhishek Gadia

Mainak Chaudhuri

Implementing a Parallel Matrix Factorization Library on the Cell Broadband Engine

The IBM Cell Broadband Engine (BE) is a novel multi-core chip with the potential for the demanding floating point performance that is required for high-fidelity scientific simulations. However, data movement within the chip can be a major challenge to realizing the benefits of the peak floating point rates. In this paper, we present the results of implementing Sweep3D on the Cell/B.E. using an intra-chip message passing model that minimizes data movement. We compare the advantages/disadvantages of this programming model with a previous implementation using a master&#x2013;worker threading strategy. We apply a previously validated micro-architecture performance model for the application executing on the Cell/B.E. (based on our previous work in Monte Carlo performance models), that predicts overall CPI (cycles per instruction), and gives a detailed breakdown of processor stalls. Finally, we use the micro-architecture model to assess the performance of future design parameters for the Cell/B.E. micro-architecture. The methodologies and results have broader implications that extend to multi-core architectures.

Olaf Lubeck

Michael Lang

Ram Srinivasan

Greg Johnson

Implementation and Performance Modeling of Deterministic Particle Transport (Sweep3D) on the IBM Cell/B.E.

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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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Scientific Programming

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Scientific Programming

High Performance Computing with the Cell Broadband Engine

High Performance Computing with the Cell Broadband Engine

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