Performance Evaluation of the Cray X1 Distributed Shared-Memory Architecture

Dunigan, T.H.; Vetter, Jeffrey S.; White, James Boyd; Worley, P.H.

doi:10.1109/mm.2005.20

Cited by 38 publications

(10 citation statements)

References 6 publications

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“…The first architecture is called the Symmetric Multiprocessor (SMP) architecture which has many identical processors and all of processors have equal access times to all memory regions of the system. The second architecture is called Non-Uniform Memory Access (NUMA) architecture [5] as shown Fig. 1.…”

Section: Numa Cluster Architecturementioning

confidence: 99%

Adaptive Computing Library for Quantum Monte Carlo Simulations

Bozkuş¹,

Anbar²,

El‐Ghazawi³

2014

IJCTE

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Abstract-Quantum Monte Carlo (QMC) methods are used in many scientific computer simulation as their core kernels. The implementation of QMC for distributed NUMA clusters may have load balancing issues at petascale level because of its random nature. We are studying on a simulation for inhomogeneous ultra-cold atoms on optical lattice, for which we developed a QMC algorithm with hybrid MPI+OpenMP programming model. This hybrid model uses the nested parallelism such that the outer loops are parallelized by MPI, while the inner loop relies on OpenMP parallelism. In this work, we presented an adaptive computing approach which learns the system work load dynamically by using our Adaptive Computing Library at run-time and then creates sufficient amount of OpenMP threads based on the availability of the system resources during the execution. The implementation shows that our adaptive approach can get very good load balancing without unnecessary overheads and can significantly provide performance increases up to 20% increases in comparison to MPI-only implementation on a XE6m Cray super computer.Index Terms-Hybrid parallel programming, load balancing, QMC simulation. I. INTRODUCTIONMany scientific simulations use quantum Monte Carlo method (QMC) at their most time consuming kernels. QMC method provides an accurate description of many-body physics which can be applied to problems relevant to chemistry, biology, physics, material science and even drug design at molecular level. In our collaborative and interdisciplinary work with physics department of George Town University, we want to build scalable and efficient an optical lattice simulator with ultra cold atoms by using inhomogeneous dynamical-mean field theory (IDMFT) in which again most time consuming portions of the simulation are QMC methods [1], [2].These calculations are computationally intensive and require very large high performance computing systems to able to study realistic simulations. Thus, the simulator is originally written with FORTRAN+MPI programming model to run distributed computer cluster with manager-worker paradigm. However, we observed that load balancing problem occurred since each MPI process performing QMC which is random by nature. We implemented the hybrid MPI+OpenMP programming to overcome load balancing issues. However, this cause memory stalling problem on Cray XE6m, where each compute node has non-uniform memory access (NUMA) memory design. When the system is fully busy with MPI processes [3] by launching new OpenMP threads [4] which stresses the memory bandwidth, cause starvation to some threads. To overcome this difficulty, we design Adaptive Computing Library (ACL) which can be used to improve the hybrid MPI+OpenMP or MPI+thread programming model.The cluster system, which consists of shared memory nodes with several multi-core CPUs connected to a high speed network to comprise a distributed memory system, is the most widely available hardware architecture for the high-performance computing community. On these systems, the hybrid para...

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Section: Numa Cluster Architecturementioning

confidence: 99%

Adaptive Computing Library for Quantum Monte Carlo Simulations

Bozkuş¹,

Anbar²,

El‐Ghazawi³

2014

IJCTE

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“…Vector-type systems are equipped with vector processors that have several cores specialized for vector calculations with high memory bandwidth. The vector-type systems can calculate sets of data elements at the same time [5] [6].…”

Section: Introductionmentioning

confidence: 99%

A Directive Generation Approach to High Code-Maintainability for Various HPC Systems

Komatsu

Egawa

Takizawa

et al. 2017

IJNC

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The emergence of various high-performance computing (HPC) systems compels users to write a code considering the characteristic of each HPC system. To describe the system-dependent information without drastic code modifications, the directive sets such as the OpenMP directive set and the OpenACC directive set are proofed to be useful. However, the code becomes complex to achieve high performance on various HPC systems because different directive sets are required for various HPC systems. Thus, the code-maintainability and readability are degraded. This paper proposes a directive generation approach that generates various kinds of directive sets using user-defined rules. Instead of using several kinds of directive sets, users only have to write special placeholders that are utilized to specify a unique code pattern where several directives are inserted. Then, the special placeholders trigger the generation of appropriate directives for each system using a user-defined rule with a code transformation framework Xevolver. Because only special placeholders are inserted in the code, the proposed approach can keep the code-maintainability and readability. From the performance evaluations of directivebased implementations on various HPC systems, it is shown that the best implementation is different among the HPC systems. Then, through the demonstration of transformation into multiple kinds of implementations, the proposed approach can successfully generate directives from a smaller number of special placeholders. Therefore, it is clarified that the proposed directive generation approach is effective to keep the maintainability of a code to be executed on various HPC systems.

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“…Some researchers prefer to write scientific applications in UPC rather than using C with MPI [3] since UPC is easier to use and can provide good performance [4], [5]. Providing a productive programming environment for UPC will encourage new scientific applications to be written in UPC instead of MPI.…”

Section: Introductionmentioning

confidence: 99%

Evaluating error detection capabilities of UPC run-time systems

Luecke

Coyle

Hoekstra

et al. 2009

Proceedings of the Third Conference on Partitioned Global Address Space Programing Models

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Abstract-The ability of system software to detect run-time errors and issue messages that help programmers quickly fix these errors is an important productivity criterion for developing and maintaining application programs. To evaluate this capability for Unified Parallel C (UPC), over two thousand run-time error tests and a run-time error detection (RTED) evaluation tool have been developed. For each error message issued, the RTED evaluation tool assigns a score from 0 to 5 based on the usefulness of the information in the message to help a programmer quickly fix the error. The RTED evaluation tool calculates averages over each error category and then prints the results. All tests and the RTED evaluation tool are freely available at the RTED web site http://rted.public.iastate.edu/UPC. The Cray, Berkeley, HP and GNU UPC compilers have been evaluated and results posted on this same web site.

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Performance Evaluation of the Cray X1 Distributed Shared-Memory Architecture

Cited by 38 publications

References 6 publications

Adaptive Computing Library for Quantum Monte Carlo Simulations

Adaptive Computing Library for Quantum Monte Carlo Simulations

A Directive Generation Approach to High Code-Maintainability for Various HPC Systems

Evaluating error detection capabilities of UPC run-time systems

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