Abstract Cloud computing environments are now widely available and are being increasingly utilized for technical computing. They are also being touted for high‐performance computing (HPC) applications in science and engineering. For example, Amazon Elastic Compute Cloud (EC2) Services offers specialized Cluster Compute instance types to run HPC applications. In this paper, we compare the performance characteristics of two Amazon EC2 HPC instance types with that of National Aeronautics and Space Administration's (NASA) Pleiades supercomputer, an SGI® ICE™ cluster. For this study, we utilized the HPC Challenge kernels and the NAS Parallel Benchmarks along with four full‐scale applications from the repertoire of codes that are being used by NASA scientists and engineers. We compare the total runtime of these codes for varying number of cores. We also break out the computation and communication times for a subset of these applications to explore the effect of interconnect differences on the two systems. In general, the single node performance of the two platforms is equivalent. However, for most of the codes when scaling to larger core counts, the performance of the EC2 HPC instances generally lags that of Pleiades because of worse network performance of the former. In addition to analyzing application performance, we also briefly touch upon the overhead due to virtualization and the usability of cloud environments such as Amazon EC2. Published 2013. This article is a U.S. Government work and is in the public domain in the U.S.A.

Intel recently introduced the Xeon Phi coprocessor based on the Many Integrated Core architecture featuring 60 cores with a peak performance of 1.0 Tflop/s. NASA has deployed a 128-node SGI Rackable system where each node has two Intel Xeon E2670 8-core Sandy Bridge processors along with two Xeon Phi 5110P coprocessors. We have conducted an early performance evaluation of the Xeon Phi. We used microbenchmarks to measure the latency and bandwidth of memory and interconnect, I/O rates, and the performance of OpenMP directives and MPI functions. We also used OpenMP and MPI versions of the NAS Parallel Benchmarks along with two production CFD applications to test four programming modes: offload, processor native, coprocessor native and symmetric (processor plus coprocessor). In this paper we present preliminary results based on our performance evaluation of various aspects of a Phi-based system.

Intel provides Hyper-Threading (HT) in processors based on its Pentium and Nehalem micro-architecture such as the Westmere-EP. HT enables two threads to execute on each core in order to hide latencies related to data access. These two threads can execute simultaneously, filling unused stages in the functional unit pipelines. To aid better understanding of HT-related issues, we collect Performance Monitoring Unit (PMU) data (instructions retired; unhalted core cycles; L2 and L3 cache hits and misses; vector and scalar floating-point operations, etc.). We then use the PMU data to calculate a new metric of efficiency in order to quantify processor resource utilization and make comparisons of that utilization between single-threading (ST) and HT modes. We also study performance gain using unhalted core cycles, code efficiency of using vector units of the processor, and the impact of HT mode on various shared resources like L2 and L3 cache. Results using four full-scale, production-quality scientific applications from computational fluid dynamics (CFD) used by NASA scientists indicate that HT generally improves processor resource utilization efficiency, but does not necessarily translate into overall application performance gain.

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 quad-core 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.

The suitability of next-generation high-performance computing systems for petascale simulations will depend on various performance factors attributable to processor, memory, local and global network, and input/output characteristics. In this paper, we evaluate performance of new dual-core SGI Altix 4700, quad-core SGI Altix ICE 8200, and dual-core IBM POWER5+ systems. To measure performance, we used micro-benchmarks from High Performance Computing Challenge (HPCC), NAS Parallel Benchmarks (NPB), and four real-world applications- three from computational fluid dynamics (CFD) and one from climate modeling. We used the micro-benchmarks to develop a controlled understanding of individual system components, then analyzed and interpreted performance of the NPBs and applications. We also explored the hybrid programming model (MPI+OpenMP) using multi-zone NPBs and the CFD application OVERFLOW-2. Achievable application performance is compared across the systems. For the ICE platform, we also investigated the effect of memory bandwidth on performance by testing 1, 2, 4, and 8 cores per node.

Columbia is a 10,240-processor supercluster consisting of 20 Altix nodes with 512 processors each, and currently ranked as one of the fastest computers in the world. In this paper, we present the performance characteristics of Columbia obtained on up to four computing nodes interconnected via the InfiniBand and/or NUMAlink4 communication fabrics. We evaluate floatingpoint performance, memory bandwidth, message passing communication speeds, and compilers using a subset of the HPC Challenge benchmarks, and some of the NAS Parallel Benchmarks including the multi-zone versions. We present detailed performance results for three scientific applications of interest to NASA, one from molecular dynamics, and two from computational fluid dynamics. Our results show that both the NUMAlink4 and In- finiBand interconnects hold promise for multi-node application scaling to at least 2048 processors.

Summary form only given. We describe a new suite of computational benchmarks that models applications featuring multiple levels of parallelism. Such parallelism is often available in realistic flow computations on systems of meshes, but had not previously been captured in benchmarks. The new suite, named NPB (NAS parallel benchmarks) multizone, is extended from the NPB suite, and involves solving the application benchmarks LU, BT and SP on collections of loosely coupled discretization meshes. The solutions on the meshes are updated independently, but after each time step they exchange boundary value information. This strategy provides relatively easily exploitable coarse-grain parallelism between meshes. Three reference implementations are available: one serial, one hybrid using the message passing interface (MPI) and OpenMP, and another hybrid using a shared memory multilevel programming model (SMP+OpenMP). We examine the effectiveness of hybrid parallelization paradigms in these implementations on three different parallel computers. We also use an empirical formula to investigate the performance characteristics of the hybrid parallel codes.

Porting applications to new high performance parallel and distributed computing platforms is a challenging task. Since writing parallel code by hand is time consuming and costly, porting codes would ideally be automated by using some parallelization tools and compilers. In this paper, we compare the performance of three parallelization tools and compilers based on the NAS Parallel Benchmark and a CFD application, ARC3D, on the SGI Origin2000 multiprocessor. The tools and compilers compared include: 1) CAPTools: an interactive computer aided parallelization toolkit, 2) Portland Group's HPF compiler, and 3) the MIPSPro FORTRAN compiler available on the Origin2000, with support for shared memory multiprocessing directives and MP runtime library. The tools and compilers are evaluated in four areas: 1) required user interaction, 2) limitations, 3) portability and 4) performance. Based on these results, a discussion on the feasibility of computer-aided parallelization of aerospace applications is presented along with suggestions for future work.