Introducing Grid Speedup Γ : A Scalability Metric for Parallel Applications on the Grid

Hoekstra, Alfons G.; Sloot, P.M.A.

doi:10.1007/11508380_26

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…Our findings confirm that our solutions to these two grand challenge problems do, in fact, scale on computational grids and can effectively tolerate the heterogeneity inherent in computational grids that plague most applications also covetous of grids' computational power and memory capacity. Hoekstra and Sloot [24] provide an instructive framework for understanding the performance of parallel applications in a homogeneous Grid environment. We are further analyzing the results although the non-homogeneity of the TeraGrid presents challenges for measuring the parameters.…”

Section: Discussionmentioning

confidence: 99%

Grid solutions for biological and physical cross-site simulations on the TeraGrid

Dong

Karonis

Karniadakis

2006

Proceedings 20th IEEE International Parallel &Amp; Distributed Processing Symposium

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Computational grids and grid middleware offer unprecedented computational power and storage capacity, and thus, have opened the possibility of solving problems that were previously not possible on even the largest single computational resources. These opportunities notwithstanding, the development of grid applications that run efficiently remains a challenge due to the inherent heterogeneity of networks and system architectures inherent in such environments. We present grid solutions to two grand challenge problems in computational mechanics. To study the scalability of our solutions we implemented both as MPI applications and ran them on the TeraGrid using NEKTAR and MPICH-G2. We present the results of our study which demonstrate near linear scalability in both applications when run across multiple TeraGrid sites and at a scale of hundreds or processors. Grid ComputingThe National Science Foundation's TeraGrid (TG) (http://www.teragrid.org) integrates the most powerful open resources in the US, which at present amount to about 50 teraflops in processing power and 1.5 petabytes of online storage connected with 40 Gb/s network. Unlike conventional supercomputers, it offers the opportunity for potentially unlimited scalability. The key question that computational scientists are faced with, however, is how to adapt their application to such complex and heterogeneous network effectively. We are, indeed, at a crossroads in parallel scientific computing, similar to what computational scientists went through about fifteen years ago. The emergence of parallel software, (e.g., MPI and OpenMP), and also of domain decomposition algorithms and corresponding freeware, (e.g., METIS) [14], made parallel computing available to the wider scientific community and allowed first-principles simulations of turbulence at very fine scales, of blood flow in the human heart [15], and of global climate at just a few km-level resolution.On the other hand, simulations designed to capture detailed physicochemical, mechanical or biological processes have demonstrated quite different characteristics [2,4,5,17,18]. Some applications are computation intensive, requiring extremely powerful computing systems. Others are data intensive [1, 3, 16], necessitating creation or mining multi-terabyte data archives to extract scientific insight. Large-scale biological and physical simulations are extremely computation intensive, and are usually characterized by tightly-coupled computations and communications. To efficiently and effectively harness the power of grid computing, it is necessary to design and adapt applications to exploit ensembles of supercomputers and match application requirements and characteristics with grid resources.The challenges in the development of such gridenabled applications lie primarily in the high degree of system heterogeneity and dynamic behavior in architecture and performance of the Grid environment. For example, a grid may have a highly heterogeneous and unbalanced communication network, whose bandwidth and latency charac...

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Section: Discussionmentioning

confidence: 99%

Grid solutions for biological and physical cross-site simulations on the TeraGrid

Dong

Karonis

Karniadakis

2006

Proceedings 20th IEEE International Parallel &Amp; Distributed Processing Symposium

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“…The component-based approach provides a convenient way to design suitable partitions for both loosely-coupled and tightlycoupled applications. Grids, by providing large computational infrastructures, allow new categories of problems to be solved [11]. For instance, the Jem3D application can solve problems with mesh sizes over 200*200*200, which is impossible on a single cluster with machine equipped of 2GB RAM, because of memory problems.…”

Section: Discussionmentioning

confidence: 99%

Performance and Scalability of a Component-Based Grid Application

Parlavantzas

Morel

Getov

et al. 2007

2007 IEEE International Parallel and Distributed Processing Symposium

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“…The performance on the G3 using all three sites is given by the thin solid line with triangles. For all problem sizes N we have measured, the grid speedup Γ [16] is less than 0.15, indicating that the performance is dominated by network communication. The network communication time scales better than the force calculation time, therefore, if N is sufficiently large, force calculation time will overtake the network communication time.…”

Section: Grid Of Pcs With Grapementioning

confidence: 97%

Simulating N-Body Systems on the Grid Using Dedicated Hardware

Groen

Zwart

McMillan

et al. 2008

Computational Science – ICCS 2008

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Abstract. We present performance measurements of direct gravitational N -body simulation on the grid, with and without specialized (GRAPE-6) hardware. Our intercontinental virtual organization consists of three sites, one in Tokyo, one in Philadelphia and one in Amsterdam. We run simulations with up to 196608 particles for a variety of topologies. In many cases, high performance simulations over the entire planet are dominated by network bandwidth rather than latency. Using a global grid of GRAPEs our calculation time remains dominated by communication over the entire range of N , which was limited due to the use of three sites. Increasing the number of particles will result in a more efficient execution and for N > ∼ 2 · 10 6 we expect the computation time to overtake the communication time. We compare our results with a performance model and find that our results are in accordance with the predicted values.

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Introducing Grid Speedup Γ : A Scalability Metric for Parallel Applications on the Grid

Cited by 10 publications

References 9 publications

Grid solutions for biological and physical cross-site simulations on the TeraGrid

Grid solutions for biological and physical cross-site simulations on the TeraGrid

Performance and Scalability of a Component-Based Grid Application

Simulating N-Body Systems on the Grid Using Dedicated Hardware

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