High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP

This paper explores the pros and cons of reconfigurable computing in the form of FPGAs for high performance efficient computing. In particular, the paper presents the results of a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs), Graphics Processor Units (GPUs), and IBM’s Cell Broadband Engine (Cell BE), in the design and implementation of the widely-used Smith-Waterman pairwise sequence alignment algorithm, with general purpose processors as a base reference implementation. Comparison criteria include speed, energy consumption, and purchase and development costs. The study shows that FPGAs largely outperform all other implementation platforms on performance per watt criterion and perform better than all other platforms on performance per dollar criterion, although by a much smaller margin. Cell BE and GPU come second and third, respectively, on both performance per watt and performance per dollar criteria. In general, in order to outperform other technologies on performance per dollar criterion (using currently available hardware and development tools), FPGAs need to achieve at least two orders of magnitude speed-up compared to general-purpose processors and one order of magnitude speed-up compared to domain-specific technologies such as GPUs

CBESW: Sequence Alignment on the Playstation 3

<p>Abstract</p> <p>Background</p> <p>The exponential growth of available biological data 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 exponentially as well. The recent emergence of accelerator technologies has made it possible to achieve an excellent improvement in execution time for many bioinformatics applications, compared to current general-purpose platforms. In this paper, we demonstrate how the PlayStation^®3, powered by the Cell Broadband Engine, can be used as a computational platform to accelerate the Smith-Waterman algorithm.</p> <p>Results</p> <p>For large datasets, our implementation on the PlayStation^®3 provides a significant improvement in running time compared to other implementations such as SSEARCH, Striped Smith-Waterman and CUDA. Our implementation achieves a peak performance of up to 3,646 MCUPS.</p> <p>Conclusion</p> <p>The results from our experiments demonstrate that the PlayStation^®3 console can be used as an efficient low cost computational platform for high performance sequence alignment applications.</p

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

<p>Abstract</p> <p>Background</p> <p>Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks.</p> <p>Results</p> <p>We evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from <url>http://cbe.ipk-gatersleben.de</url>.</p> <p>Conclusions</p> <p>The results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics.</p

Can we apply accelerator-cores to control-intensive programs?

There is a trend towards using accelerators to increase performance and energy efficiency of general-purpose processors. So far, most accelerators have been build with HPC-applications in mind. A question that arises is how well can other applications benefit from these accelerators? In this paper, we discuss the acceleration of three benchmarks using the SPUs of a Cell-BE. We analyze the potential speedup given the inherent parallelism in the applications. While the potential speedup is significant in all benchmarks, the obtained speedup lags behind due to a mismatch between micro-architectural properties of the accelerators and the benchmark properties

Dynamic Multigrain Parallelization on the Cell Broadband Engine

This paper addresses the problem of orchestrating and scheduling parallelism at multiple levels of granularity on heterogeneous multicore processors. We present policies and mechanisms for adaptive exploitation and scheduling of multiple layers of parallelism on the Cell Broadband Engine. Our policies combine event-driven task scheduling with malleable loop-level parallelism, which is exposed from the runtime system whenever task-level parallelism leaves cores idle. We present a runtime system for scheduling applications with layered parallelism on Cell and investigate its potential with RAxML, a computational biology application which infers large phylogenetic trees, using the Maximum Likelihood (ML) method. Our experiments show that the Cell benefits significantly from dynamic parallelization methods, that selectively exploit the layers of parallelism in the system, in response to workload characteristics. Our runtime environment outperforms naive parallelization and scheduling based on MPI and Linux by up to a factor of 2.6. We are able to execute RAxML on one Cell four times faster than on a dual-processor system with Hyperthreaded Xeon processors, and 5--10\% faster than on a single-processor system with a dual-core, quad-thread IBM Power5 processor

RAxML-Cell: Parallel Phylogenetic Tree Inference on the Cell Broadband Engine

Phylogenetic tree reconstruction is one of the grand challenge problems in Bioinformatics. The search for a best-scoring tree with 50 organisms, under a reasonable optimality criterion, creates a topological search space which is as large as the number of atoms in the universe. Computational phylogeny is challenging even for the most powerful supercomputers. It is also an ideal candidate for benchmarking emerging multiprocessor architectures, because it exhibits various levels of fine and coarse-grain parallelism. In this paper, we present the porting, optimization, and evaluation of RAxML on the Cell Broadband Engine. RAxML is a provably efficient, hill climbing algorithm for computing phylogenetic trees based on the Maximum Likelihood (ML) method. The algorithm uses an embarrassingly parallel search method, which also exhibits data-level parallelism and control parallelism in the computation of the likelihood functions. We present the optimization of one of the currently fastest tree search algorithms, on a real Cell blade prototype. We also investigate problems and present solutions pertaining to the optimization of floating point code, control flow, communication, scheduling, and multi-level parallelization on the Cell

Exploring New Search Algorithms and Hardware for Phylogenetics: RAxML Meets the IBM Cell

Phylogenetic inference is considered to be one of the grand challenges in Bioinformatics due to the immense computational requirements. RAxML is currently among the fastest and most accurate programs for phylogenetic tree inference under the Maximum Likelihood (ML) criterion. First, we introduce new tree search heuristics that accelerate RAxML by a factor of 2.43 while returning equally good trees. The performance of the new search algorithm has been assessed on 18 real-world datasets comprising 148 up to 4,843 DNA sequences. We then present the implementation, optimization, and evaluation of RAxML on the IBM Cell Broadband Engine. We address the problems and provide solutions pertaining to the optimization of floating point code, control flow, communication, and scheduling of multi-level parallelism on the Cel

Revisiting the Speed-versus-Sensitivity Tradeoff in Pairwise Sequence Search

The Smith-Waterman algorithm is a dynamic programming method for determining optimal local alignments between nucleotide or protein sequences. However, it suffers from quadratic time and space complexity. As a result, many algorithmic and architectural enhancements have been proposed to solve this problem, but at the cost of reduced sensitivity in the algorithms or significant expense in hardware, respectively. Hence, there exists a need to evaluate the tradeoffs between the different solutions. This motivation, coupled with the lack of an evaluation metric to quantify these tradeoffs leads us to formally define and quantify the sensitivity of homology search methods so that tradeoffs between sequence-search solutions can be evaluated in a quantitative manner. As an example, though the BLAST algorithm executes significantly faster than Smith-Waterman, we find that BLAST misses 80% of the significant sequence alignments. This paper then presents a highly efficient parallelization of the Smith-Waterman algorithm on the Cell Broadband Engine, a novel hybrid multicore architecture that drives the PlayStation 3 (PS3) game consoles, and emulates BLAST by repeatedly executing the parallelized Smith-Waterman algorithm to search for a query in a given sequence database. Through an innovative mapping of the optimal Smith-Waterman algorithm onto a cluster of PlayStation 3 nodes, our implementation delivers a 10-fold speed-up over a high-end multicore architecture and an 88-fold speed-up over a non-accelerated PS3. Finally, we compare the performance of our implementation of the Smith-Waterman algorithm to that of BLAST and the canonical Smith-Waterman implementation, based on a combination of three factors — execution time (speed), sensitivity, and the actual cost of de-ploying each solution. In the end, our parallelized Smith-Waterman algorithm approaches the speed of BLAST while maintaining ideal sensitivity and achieving low cost through the use of PlayStation 3 game consoles

Elliptic Curve Cryptography on Modern Processor Architectures

Abstract Elliptic Curve Cryptography (ECC) has been adopted by the US National Security Agency (NSA) in Suite "B" as part of its "Cryptographic Modernisation Program ". Additionally, it has been favoured by an entire host of mobile devices due to its superior performance characteristics. ECC is also the building block on which the exciting field of pairing/identity based cryptography is based. This widespread use means that there is potentially a lot to be gained by researching efficient implementations on modern processors such as IBM's Cell Broadband Engine and Philip's next generation smart card cores. ECC operations can be thought of as a pyramid of building blocks, from instructions on a core, modular operations on a finite field, point addition & doubling, elliptic curve scalar multiplication to application level protocols. In this thesis we examine an implementation of these components for ECC focusing on a range of optimising techniques for the Cell's SPU and the MIPS smart card. We show significant performance improvements that can be achieved through of adoption of EC