nils-herrmann/clean_scirepeval huggingface dataset:
Performance evaluation of the SX‐6 vector architecture for scientific computations

The growing gap between sustained and peak performance for scientific applications is a well‐known problem in high‐performance computing. The recent development of parallel vector systems offers the potential to reduce this gap for many computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX‐6 vector processor, and compares it against the cache‐based IBM Power3 and Power4 superscalar architectures, across a number of key scientific computing areas. First, we present the performance of a microbenchmark suite that examines many low‐level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks. Finally, we evaluate the performance of several scientific computing codes. Overall results demonstrate that the SX‐6 achieves high performance on a large fraction of our application suite and often significantly outperforms the cache‐based architectures. However, certain classes of applications are not easily amenable to vectorization and would require extensive algorithm and implementation reengineering to utilize the SX‐6 effectively.
Million-to-Billion Atom Simulation of Chemical Reactions : Embedded Divide-and-Conquer and Hierarchical Cellular Decomposition Frameworks for Scalable Scientific Computing

Simulating chemical reactions involving billions of atoms has been a dream of scientists, with broad societal impacts. This paper realizes the dream through novel simulation methods, algorithms, and parallel computing and visualization techniques. We have designed O(N) embedded divide-and-conquer (EDC) algorithms for 1) first principles-based parallel reactive force-field (P-ReaxFF) molecular dynamics (MD), and 2) density functional theory (DFT) on adaptive multigrids for quantum mechanical MD, based on a space-time multiresolution MD (MRMD) algorithm. To map these O(N) algorithms onto parallel computers, we have developed a hierarchical cellular decomposition (HCD) framework, including 1) wavelet-based computational-space decomposition for adaptive load balancing, and 2) octree-based probabilistic visibility culling for interactive visualization of billion-atom datasets. Preliminary tests on 1,920 Itanium2 processors of the NASA Columbia supercomputer have achieved unprecedented scales of reactive atomistic simulations: 0.56 billion-atom P-ReaxFF and 1.4 million-atom (0.12 trillion grid points) EDC-DFT simulations, in addition to 18.9 billion-atom MRMD simulation.
Impact of the Columbia Supercomputer on NASA Science and Engineering Applications

Columbia is a 10,240-processor supercomputer consisting of 20 Altix nodes with 512 processors each, and currently ranked as one of the fastest in the world. In this paper, we briefly describe the Columbia system and its supporting infrastructure, the underlying Altix architecture, and benchmark performance on up to four nodes interconnected via the InfiniBand and NUMAlink4 communication fabrics. Additionally, three science and engineering applications from different disciplines running on multiple Columbia nodes are described and their performance results are presented. Overall, our results show promise for multi-node application scaling, allowing the ability to tackle compute-intensive scientific problems not previously solvable on available supercomputers.
Integrated performance monitoring of a cosmology application on leading HEC platforms

The cosmic microwave background (CMB) is an exquisitely sensitive probe of the fundamental parameters of cosmology. Extracting this information is computationally intensive, requiring massively parallel computing and sophisticated numerical algorithms. In this work we present MADbench, a lightweight version of the MADCAP CMB power spectrum estimation code that retains the operational complexity and integrated system requirements. In addition, to quantify communication behavior across a variety of architectural platforms, we introduce the integrated performance monitoring (IPM) package: a portable, lightweight, and scalable tool for effectively extracting MPI message-passing overheads. A performance characterization study is conducted on some of the world's most powerful supercomputers, including the superscalar Seaborg (IBM Power3+) and CC-NUMA Columbia (SGIAltix), as well as the vector-based Earth Simulator (NEC SX-6 enhanced) and Phoenix (Cray XI) systems. In-depth analysis shows that in order to bridge the gap between theoretical and sustained system performance, it is critical to gain a clear understanding of how the distinct parts of large-scale parallel applications interact with the individual subcomponents of HEC platforms.
Performance evaluation of the SX-6 vector architecture for scientific computations: Research Articles

The growing gap between sustained and peak performance for scientific applications is a well-known problem in high-performance computing. The recent development of parallel vector systems offers the potential to reduce this gap for many computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX-6 vector processor, and compares it against the cache-based IBM Power3 and Power4 superscalar architectures, across a number of key scientific computing areas. First, we present the performance of a microbenchmark suite that examines many low-level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks. Finally, we evaluate the performance of several scientific computing codes. Overall results demonstrate that the SX-6 achieves high performance on a large fraction of our application suite and often significantly outperforms the cache-based architectures. However, certain classes of applications are not easily amenable to vectorization and would require extensive algorithm and implementation reengineering to utilize the SX-6 effectively.
NAS Experience with the Cray X1

A Cray X1 computer system was installed at the NASA Advanced Supercomputing (NAS) facility at NASA Ames Research Center in 2004. An evaluation study of this unique high performance computing (HPC) architecture, from the standpoints of processor and system performance, ease of use, and production computing readiness tailored to the needs of the NAS scientific community, was recently completed. The results of this study are described in this article. The evaluation included system performance and characterization using a subset of the HPC Challenge benchmarks and NAS Parallel Benchmarks, as well as detailed performance on scientific applications from the computational fluid dynamics (CFD) and Earth science disciplines that represent a major portion of the computational workload at NAS. Performance results are discussed and compared with the main HPC platform at NAS — the SGI Altix.
An Application-Based Performance Characterization of the Columbia Supercluster

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.
Parallel Performance Characterization of Columbia

Using a collection of benchmark problems of increasing levels of realism and computational effort, we will characterize the strengths and limitations of the 10,240 processor Columbia system to deliver supercomputing value to application scientists. Scientists need to be able to determine if and how they can utilize Columbia to carry extreme workloads, either in terms of ultra-large applications that cannot be run otherwise (capability), or in terms of very large ensembles of medium-scale applications to populate response matrices (capacity). We select existing application benchmarks that scale from a small number of processors to the entire machine, and that highlight different issues in running supercomputing-calss applicaions, such as the various types of memory access, file I/O, inter- and intra-node communications and parallelization paradigms.
Job scheduling in a heterogenous grid environment

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and three job migration algorithms. The architecture is scalable and does not assume control of local site resources. The job migration policies use the availability and performance of computer systems, the network bandwidth available between systems, and the volume of input and output data associated with each job. An extensive performance comparison is presented using real workloads from leading computational centers. The results, based on several key metrics, demonstrate that the performance of our distributed migration algorithms is significantly greater than that of a local scheduling framework and comparable to a non-scalable global scheduling approach.
FastMap: a distributed scheme for mapping large scale applications onto computational grids

Many large and complex computational applications can be modeled as irregular graphs and are typically characterized by a large number of vertices and edges. This paper proposes a new and fast mapping heuristic, called FastMap, to map this class of applications onto heterogeneous metacomputing platforms such as computational grids. While previous approaches have delved into graph partitioning of the application, we attempt to solve this problem from the clustering perspective. We exploit a hierarchical resource management infrastructure on the grid to distribute the overhead of mapping among a tree of schedulers and develop a scheme that proves to be almost linear in its scalability. Furthermore, we optimize on the result of the mapping with the help of a genetic algorithm at each scheduler node. Our experiments include a 50,000-node application graph from NASA and several other synthetically-generated graphs with as many as 100,000 vertices. Comparisons with another heterogeneous partitioner, MiniMax, show an improvement factor of over 100 on the mapping time, yet with superior quality mapping.
Unstructured Adaptive (UA) NAS Parallel Benchmark. Version 1.0

We present a complete specification of a new benchmark for measuring the performmce of modern computer systems when solving scientific F iobhiS fsaaiiing irregular, dynamic memory accesses. It complements the existing NAS Parallel Benchmark suite. The benchmark involves the solution of a stylized heat transfer problem in a cubic domain, discretized on an adaptively refined, unstructured mesh.
Scheduling in Heterogeneous Grid Environments: The Effects of DataMigration

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this goal can be fully realized. One problem critical to the effective utilization of computational grids is efficient job scheduling. Our prior work addressed this challenge by defining a grid scheduling architecture and several job migration strategies. The focus of this study is to explore the impact of data migration under a variety of demanding grid conditions. We evaluate our grid scheduling algorithms by simulating compute servers, various groupings of servers into sites, and inter-server networks, using real workloads obtained from leading supercomputing centers. Several key performance metrics are used to compare the behavior of our algorithms against reference local and centralized scheduling schemes. Results show the tremendous benefits of grid scheduling, even in the presence of input/output data migration - while highlighting the importance of utilizing communication-aware scheduling schemes.
A Performance Evaluation of the Cray X1 for Scientific Applications

The last decade has witnessed a rapid proliferation of superscalar cache-based microprocessors to build high-end capability and capacity computers primarily because of their generality, scalability, and cost effectiveness. However, the recent development of massively parallel vector systems is having a significant effect on the supercomputing landscape. In this paper, we compare the performance of the recently-released Cray X1 vector system with that of the cacheless NEC SX-6 vector machine, and the superscalar cache-based IBM Power3 and Power4 architectures for scientific applications. Overall results demonstrate that the X1 is quite promising, but performance improvements are expected as the hardware, systems software, and numerical libraries mature. Code reengineering to effectively utilize the complex architecture may also lead to significant efficiency enhancements.
A hierarchical and distributed approach for mapping large applications to heterogeneous grids using genetic algorithms

In this paper, we propose a distributed approach for mapping a single large application to a heterogeneous grid environment. To minimize the execution time of the parallel application, we distribute the mapping overhead to the available nodes of the grid. This approach not only provides a fast mapping of tasks to resources but is also scalable. We adopt a hierarchical grid model and accomplish the job of mapping tasks to this topology using a scheduler tree. Results show that our three-phase algorithm provides high quality mappings, and is fast and scalable.
Job Superscheduler Architecture and Performance in Computational Grid Environments

Computational grids hold great promise in utilizing geographically separated heterogeneous resources to solve large-scale complex scientific problems. However, a number of major technical hurdles, including distributed resource management and effective job scheduling, stand in the way of realizing these gains. In this paper, we propose a novel grid superscheduler architecture and three distributed job migration algorithms. We also model the critical interaction between the superscheduler and autonomous local schedulers. Extensive performance comparisons with ideal, central, and local schemes using real workloads from leading computational centers are conducted in a simulation environment. Additionally, synthetic workloads are used to perform a detailed sensitivity analysis of our superscheduler. Several key metrics demonstrate that substantial performance gains can be achieved via smart superscheduling in distributed computational grids.
Performance of a heterogeneous grid partitioner for N-body applications

An important characteristic of distributed grids is that they allow geographically separated multicomputers to be tied together in a transparent virtual environment to solve large-scale computational problems. However, many of these applications require effective runtime load balancing for the resulting solutions to be viable. Recently, we developed a latency tolerant partitioner, called MinEX, specifically for use in distributed grid environments. We compare the performance of MinEX to that of METIS using simulated heterogeneous grid configurations. A solver for the classical N-body problem is implemented to provide a benchmark for the comparisons. Simulation results show that MinEX provides superior quality partitions while being competitive to METIS in speed of execution
An analysis of performance enhancement techniques for overset grid applications

The overset grid methodology has significantly reduced time-to-solution of high-fidelity computational fluid dynamics (CFD) simulations about complex aerospace configurations. The solution process resolves the geometrical complexity of the problem domain by using separately generated but overlapping structured discretization grids that periodically exchange information through interpolation. However, high performance computations of such large-scale realistic applications must be handled efficiently on state-of-the-art parallel supercomputers. This paper analyzes the effects of various performance enhancement techniques on the parallel efficiency of an overset grid Navier-Stokes CFD application running on an SGI Origin2000 machine. Specifically, the role of asynchronous communication, grid splitting, and grid grouping strategies are presented and discussed. Results indicate that performance depends critically on the level of latency hiding and the quality of load balancing across the processors.
High End Computing Technologies for Earth Science Applications: Trends, Challenges, and Innovations

Earth science applications of the future will stress the capabilities of even the highest performance supercomputers in the areas of raw compute power, mass storage management, and software environments. These NASA mission critical problems demand usable multi-petaflops and exabyte-scale systems to fully realize their science goals. With an exciting vision of the technologies needed, NASA has established a comprehensive program of advanced research in computer architecture, software tools, and device technology to ensure that, in partnership with US industry, it can meet these demanding requirements with reliable, cost effective, and usable ultra-scale systems. NASA will exploit, explore, and influence emerging high end computing architectures and technologies to accelerate the next generation of engineering, operations, and discovery processes for NASA Enterprises. This article captures this vision and describes the concepts, accomplishments, and the potential payoff of the key thrusts that will help meet the computational challenges in Earth science applications.
Performance Analysis of a Hybrid Overset Multi-block Application on Multiple Architectures

This paper presents a detailed performance analysis of a multi-block overset grid CFD application on multiple state-of-the-art computer architectures. The application is implemented using a hybrid MPI+OpenMP programming paradigm that exploits both coarse and fine-grain parallelism. The hybrid model also extends the applicability of multi-block programs to large clusters of SMP nodes. Extensive investigations were conducted on Cray SX-6, IBM Power3 and Power4, and SGI Origin3000 platforms. Overall results for complex vortex dynamics simulations demonstrate that the SX-6 achieves the highest performance and outperforms the RISC-based architectures; however, the best scaling performance was achieved on the Power3.
High‐performance Java codes for computational fluid dynamics

The computational science community is reluctant to write large‐scale computationally‐intensive applications in Java due to concerns over Java's poor performance, despite the claimed software engineering advantages of its object‐oriented features. Naive Java implementations of numerical algorithms can perform poorly compared to corresponding Fortran or C implementations. To achieve high performance, Java applications must be designed with good performance as a primary goal. This paper presents the object‐oriented design and implementation of two real‐world applications from the field of computational fluid dynamics (CFD): a finite‐volume fluid flow solver (LAURA, from NASA Langley Research Center) and an unstructured mesh adaptation algorithm (2D_TAG, from NASA Ames Research Center). This work builds on our previous experience with the design of high‐performance numerical libraries in Java. We examine the performance of the applications using the currently available Java infrastructure and show that the Java version of the flow solver LAURA performs almost within a factor of 2 of the original procedural version. Our Java version of the mesh adaptation algorithm 2D_TAG performs within a factor of 1.5 of its original procedural version on certain platforms. Our results demonstrate that object‐oriented software design principles are not necessarily inimical to high performance.
Designing an Efficient Partitioning Algorithm for Grid Environments with Application to N-body Problems

An important characteristic of distributed grids is that they allow geographically separated multicomputers to be tied together in a transparent virtual environment to solve large-scale computational problems. However, many of these applications require effective runtime load balancing for the resulting solutions to be viable. This paper compares the performance of our MinEX latency-tolerant partitioner with METIS using simulated heterogeneous grid configurations. A solver for the classical N-body problem is implemented to provide a framework for the comparisons. Experimental results show that MinEX provides superior quality partitions and is competitive to METIS in execution speed.
Evaluation of Cache-based Superscalar and Cacheless Vector Architectures for Scientific Computations

The growing gap between sustained and peak performance for scientific applications is a well-known problem in high end computing. The recent development of parallel vector systems offers the potential to bridge this gap for many computational science codes and deliver a substantial increase in comput-ing capabilities. This paper examines the intranode performance of the NEC SX-6 vector processor and the cache-based IBM Power3/4 superscalar architectures across a number of scientific computing areas. First, we present the performance of a microbenchmark suite that examines low-level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks. Finally, we evaluate the performance of several scientific computing codes. Results demonstrate that the SX-6 achieves high performance on a large fraction of our applications and often significantly outperforms the cache-based architectures. However, certain applications are not easily amenable to vectorization and would require extensive algorithm and implementation reengineering to utilize the SX-6 effectively.
Load Balancing Strategies for Multi-Block Overset Grid Applications

The multi-block overset grid method is a powerful technique for high-fidelity computational fluid dynamics (CFD) simulations about complex aerospace configurations. The solution process uses a grid system that discretizes the problem domain by using separately generated but overlapping structured grids that periodically update and exchange boundary information through interpolation. For efficient high performance computations of large-scale realistic applications using this methodology, the individual grids must be properly partitioned among the parallel processors. Overall performance, therefore, largely depends on the quality of load balancing. In this paper, we present three different load balancing strategies far overset grids and analyze their effects on the parallel efficiency of a Navier-Stokes CFD application running on an SGI Origin2000 machine.