Scientific application-based performance comparison of SGI Altix 4700, IBM POWER5+, and SGI ICE 8200 supercomputers

Saini, Subhash; Talcott, Dale; Jespersen, Dennis C.; Djomehri, Jahed; Jin, Haoqiang; Biswas, Rupak

doi:10.1109/sc.2008.5222565

Cited by 20 publications

(15 citation statements)

References 5 publications

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“…The performance of the GoGrid clusters with the single core instances is as expected, but we observe scalability problems with the 3 core GG.xlarge instances. In comparison with previously reported results, the DGEMM performance of m1.large (c1.medium) instances is similar to that of Altix4700 (ICE) [29], and the memory bandwidth of Cray X1 (2003) is several times faster than that of the fastest cloud resource currently available [30].…”

Section: Multimachine Benchmarkssupporting

confidence: 76%

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Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing

Iosup

Ostermann

Yigitbasi

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

734

368

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Performance analysis of cloud computing services for many-tasks scientific computingIosup Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract-Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel production environments. However, the current commercial clouds have been built to support web and small database workloads, which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through tra...

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Section: Multimachine Benchmarkssupporting

confidence: 76%

“…Much work has been put into the evaluation of novel supercomputers [27], [29], [30], [31], [47], [48] and nontraditional systems [5], [32], [37], [49], [70] for scientific computing. We share much of the used methodology with previous work; we see this as an advantage in that our results are readily comparable with existing results.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing

Iosup

Ostermann

Yigitbasi

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

734

368

View full text Add to dashboard Cite

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“…Other groups have looked at multicore systems [1], [2], [4]- [6], [11], [13], [17], [18], examining contention arising from a combination of shared resources in the memory hierarchy. Our approach is distinct in that it breaks contention down to its individual components-contention for shared cache, front-side bus, HyperTransport, QuickPath Interconnect, and contention on the memory bus.…”

Section: Introductionmentioning

confidence: 99%

Performance impact of resource contention in multicore systems

Hood

Jin²,

Mehrotra³

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing (IPDPS)

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Resource sharing in commodity multicore processors can have a significant impact on the performance of production applications. In this paper we use a differential performance analysis methodology to quantify the costs of contention for resources in the memory hierarchy of several multicore processors used in high-end computers. In particular, by comparing runs that bind MPI processes to cores in different patterns, we can isolate the effects of resource sharing. We use this methodology to measure how such sharing affects the performance of four applications of interest to NASA-OVERFLOW, MITgcm, Cart3D, and NCC. We also use a subset of the HPCC benchmarks and hardware counter data to help interpret and validate our findings. We conduct our study on high-end computing platforms that use four different quadcore microprocessors-Intel Clovertown, Intel Harpertown, AMD Barcelona, and Intel Nehalem-EP. The results help further our understanding of the requirements these codes place on their production environments and also of each computer's ability to deliver performance.

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“…Ideally, one could have a large shared memory machine available on demand when needed, but also allow the hardware to serve more traditional HPC workloads when large memory requirements didn't exist. While this is not possible using an SGI UV architecture, the SGI is still known for supporting MPI workloads effectively [25].…”

Section: A Usabilitymentioning

confidence: 99%

Evaluation of SMP Shared Memory Machines for Use with In-Memory and OpenMP Big Data Applications

Younge

Reidy

Henschel

et al. 2016

2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Abstract-While distributed memory systems have shaped the field of distributed systems for decades, the demand for many-core shared memory resources is increasing. Symmetric Multiprocessor Systems (SMPs) have become increasingly important recently among a wide array of disciplines, ranging from Bioinformatics to astrophysics, and beyond. With the increase in big data computing, the size and scope of traditional commodity server systems is often outpaced. While some big data applications can be mapped to distributed memory systems found through many cluster and cloud technologies today, this effort represents a large barrier of entry that some projects cannot cross. Shared memory SMP systems look to effectively and efficiently fill this niche within distributed systems by providing high throughput and performance with minimized development effort, as the computing environment often represents what many researchers are already familiar with.In this paper we look at the use of two common shared memory systems, the ScaleMP vSMP virtualized SMP deployment at Indiana University, and the SGI UV architecture deployed at University of Arizona. While both systems are notably different in their design, their potential impact on computing are remarkably similar. As such, we look to compare each system first under a set of OpenMP threaded benchmarks via the SPEC group, and follow up with our experience using each machine for Trinity de-novo assembly. We find both SMP systems are well suited to support various big data applications, with the newer vSMP deployment often slightly faster, however certain caveats and performance considerations are necessary when considering such SMP systems.

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Scientific application-based performance comparison of SGI Altix 4700, IBM POWER5+, and SGI ICE 8200 supercomputers

Cited by 20 publications

References 5 publications

Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing

Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing

Performance impact of resource contention in multicore systems

Evaluation of SMP Shared Memory Machines for Use with In-Memory and OpenMP Big Data Applications

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