THE NAS PARALLEL BENCHMARKS

The Numerical Aerodynamic Simulation (NAS) Program, which is based at NASA Ames Research Center, is a large-scale effort to advance the state of computational aerodynamics. Specifically, the NAS organization aims &dquo;to provide the Nation’s aerospace research and development community by the year 2000 a highperformance, operational computing system capable of simulating an entire aerospace vehicle system within a computing time of one to several hours&dquo; (NAS Systems Division, 1988, p. 3). The successful solution of this &dquo;grand challenge&dquo; problem will require the development of computer systems that can perform the required complex scientific computations at a sustained rate nearly 1,000 times greater than current generation supercomputers can achieve. The architecture of computer systems able to achieve this level of performance will likely be dissimilar to the shared memory multiprocessing supercomputers of today. While no consensus yet exists on what the design will be, it is likely that the system will consist of at least 1,000 processors computing in parallel. Highly parallel systems with computing power roughly equivalent to that of traditional shared memory multiprocessors exist today. Unfortunately, for various reasons, the performance evaluation of these systems on comparable types of scientific computations is very difficult. Relevant data for the performance of algorithms of interest to the computational aerophysics community on many currently available parallel systems are limited. Benchmarking and performance evaluation of such systems have not kept pace with advances in hardware, software, and algorithms. In particular, there is as yet no generally accepted benchmark program or even a benchmark strategy for these systems

The NAS parallel benchmarks

A new set of benchmarks was developed for the performance evaluation of highly parallel supercomputers. These benchmarks consist of a set of kernels, the 'Parallel Kernels,' and a simulated application benchmark. Together they mimic the computation and data movement characteristics of large scale computational fluid dynamics (CFD) applications. The principal distinguishing feature of these benchmarks is their 'pencil and paper' specification - all details of these benchmarks are specified only algorithmically. In this way many of the difficulties associated with conventional benchmarking approaches on highly parallel systems are avoided

The NAS Parallel Benchmarks 2.1 Results

We present performance results for version 2.1 of the NAS Parallel Benchmarks (NPB) on the following architectures: IBM SP2/66 MHz; SGI Power Challenge Array/90 MHz; Cray Research T3D; and Intel Paragon. The NAS Parallel Benchmarks are a widely-recognized suite of benchmarks originally designed to compare the performance of highly parallel computers with that of traditional supercomputers

Object-Oriented Implementation of the NAS Parallel Benchmarks using Charm++

This report describes experiences with implementing the NAS Computational Fluid Dynamics benchmarks using a parallel object-oriented language, Charm++. Our main objective in implementing the NAS CFD kernel benchmarks was to develop a code that could be used to easily experiment with different domain decomposition strategies and dynamic load balancing. We also wished to leverage the object-orientation provided by the Charm++ parallel object-oriented language, to develop reusable abstractions that would simplify the process of developing parallel applications. We first describe the Charm++ parallel programming model and the parallel object array abstraction, then go into detail about each of the Scalar Pentadiagonal (SP) and Lower/Upper Triangular (LU) benchmarks, along with performance results. Finally we conclude with an evaluation of the methodology used

Iso-energy-efficiency: An approach to power-constrained parallel computation

Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making

Scalability and Performance Analysis of OpenMP Codes Using the Periscope Toolkit

In this paper, we present two new approaches while rendering necessary extensions to Periscope to perform scalability and performance analysis on OpenMP codes. Periscope is an online-based performance analysis toolkit which consists of a user defined number of analysis agents that automatically search for the performance properties while the application is running. In order to detect the scalability and performance bottlenecks of OpenMP codes using Periscope, a few newly defined performance properties and meta properties are formalized. We manifest our implementation by evaluating NAS OpenMP benchmarks. As shown in our results, our approach identifies the code regions which do not scale well and other performance problems, e.g. load imbalance in NAS parallel benchmarks