High End Computing

Over the last decade, storage systems have experienced a 10fold increase between their capacity and bandwidth. This gap is predicted to grow faster with exponentially growing concurrency levels, with future exascales delivering millions of nodes and billions of threads of execution. A critical component of future file systems for high-end computing is metadata management. This extended abstract presents ZHT, a zero-hop distributed hash-table, which has been tuned for the specific requirements of high-end computing. The primary goal of ZHT is excellent availability, fault tolerance, high throughput, and low latencies. 1.

We present a de novo hierarchical simulation framework for first-principles based predictive simulations of materials and their validation on high-end parallel supercomputers and geographically distributed clusters. In this framework, highend chemically reactive and non-reactive molecular dynamics (MD) simulations explore a wide solution space to discover microscopic mechanisms that govern macroscopic material properties, into which highly accurate quantum mechanical (QM) simulations are embedded to validate the discovered mechanisms and quantify the uncertainty of the solution. The framework includes an embedded divide-andconquer (EDC) algorithmic framework for the design of linear-scaling simulation algorithms with minimal bandwidth complexity and tight error control. The EDC framework also enables adaptive hierarchical simulation with automated model transitioning assisted by graph-based event tracking. A tunable hierarchical cellular decomposition parallelization framework then maps the O(N) EDC algorithms onto petaflops computers, while achieving performance tunability through a hierarchy of parameterized cell data/ computation structures, as well as its implementation using hybrid grid remote procedure call + message passing + threads programming. High-end computing platforms such as IBM BlueGene/L, SGI Altix 3000 and the NSF TeraGrid provide an excellent test grounds for the framework. On these platforms, we have achieved unprecedented scales of quantum-mechanically accurate and well validated, chemi-cally reactive atomistic simulations-1.06 billion-atom fast reactive force-field MD and 11.8 million-atom (1.04 trillion grid points) quantum-mechanical MD in the framework of the EDC density functional theory on adaptive multigridsin addition to 134 billion-atom non-reactive space-time multiresolution MD, with the parallel efficiency as high as 0.998 on 65,536 dual-processor BlueGene/L nodes. We have also achieved an automated execution of hierarchical QM/MD simulation on a grid consisting of 6 supercomputer centers in the US and Japan (in total of 150,000 processor hours), in which the number of processors change dynamically on demand and resources are allocated and migrated dynamically in response to faults. Furthermore, performance portability has been demonstrated on a wide range of platforms such as BlueGene/L, Altix 3000, and AMD Opteron-based Linux clusters. Downloaded from − − −4 of the bulk density. (c) Energy conservation in EDC-DFT based MD simulation of liquid Rb at 1400 K. The domain and buffer sizes are 16.424 and 8.212 a.u., respectively.

Parallel I/O is fast becoming a bottleneck to the research agendas of many users of extreme scale parallel computers. The principle cause of this is the concurrency explosion of high-end computation, coupled with the complexity of providing parallel file systems that perform reliably at such scales. More than just being a bottleneck, parallel I/O performance at scale is notoriously variable, being influenced by numerous factors inside and outside the application, thus making it extremely difficult to isolate cause and effect for performance events. In this paper, we propose a statistical approach to understanding I/O performance that moves from the analysis of performance events to the exploration of performance ensembles. Using this methodology, we examine two I/O-intensive scientific computations from cosmology and climate science, and demonstrate that our approach can identify application and middleware performance deficiencies-resulting in more than 4× run time improvement for both examined applications.

Comparisons of high-performance computers based on their peak floating point performance are common but seldom useful when comparing performance on real workloads. Factors that influence sustained performance extend beyond a system's floating-point units, and real applications exercise machines in complex and diverse ways. Even when it is possible to compare systems based on their performance, other considerations affect which machine is best for a given organization. These include the cost, the facilities requirements (power, floorspace, etc.), the programming model, the existing code base, and so on. This paper describes some of the important measures for evaluating high-performance computers. We present data for many of these metrics based on our experience at Lawrence Livermore National Laboratory (LLNL), and we compare them with published information on the Earth Simulator. We argue that evaluating systems involves far more than comparing benchmarks and acquisition costs. We show that evaluating systems often involves complex choices among a variety of factors that influence the value of a supercomputer to an organization, and that the high-end computing community should view cost/performance comparisons of different architectures with skepticism.

The Information Sciences Institute and Caltech are enabling USJFCOM and the Institute for Defense Analyses to conduct entity-level simulation experiments using hundreds of distributed computer nodes on Linux Clusters as a vehicle for simulating millions of JSAF entities. Included below is the experience with the design and implementation of the code that increased scalability, thereby enabling two orders of magnitude growth and the effective use of DoD high-end computers. A typical JSAF experiment generates several terabytes of logged data, which is queried in near-real-time and for months afterward. The amount of logged data and the desired database query performance mandated the redesign of the original logger system's monolithic database, making it distributed and incorporating several advanced concepts. System procedures and practices were established to reliably execute the globalscale simulations, effectively operate the distributed computers, efficiently process and store terabytes of data, and provide straightforward access to the data by analysts.

This paper addresses the underlying sources of performance degradation (e.g. latency, overhead, and starvation) and the difficulties of programmer productivity (e.g. explicit locality management and scheduling, performance tuning, fragmented memory, and synchronous global barriers) to dramatically enhance the broad effectiveness of parallel processing for high end computing. We are developing a hierarchical threaded virtual machine (HTVM) that defines a dynamic, multithreaded execution model and programming model, providing an architecture abstraction for HEC system software and tools development. We are working on a prototype language, LITL-X (pronounced "little-X") for Latency Intrinsic-Tolerant Language, which provides the application programmers with a powerful set of semantic constructs to organize parallel computations in a way that hides/manages latency and limits the effects of overhead. This is quite different from locality management, although the intent of both strategies is to minimize the effect of latency on the efficiency of computation. We will work on a dynamic compilation and runtime model to achieve efficient LITL-X program execution. Several adaptive optimizations will be studied. A methodology of incorporating domainspecific knowledge in program optimization will be studied. Finally, we plan to implement our method in an experimental testbed for a HEC architecture and perform a qualitative and quantitative evaluation on selected applications.

For parallel applications running on high-end computing systems, which processes of an application get launched on which processing cores is typically determined at application launch time without any information about the application characteristics. As high-end computing systems continue to grow in scale, however, this approach is becoming increasingly infeasible for achieving the best performance. For example, for systems such as IBM Blue Gene and Cray XT that rely on flat 3D torus networks, process communication often involves network sharing, even for highly scalable applications. This causes the overall application performance to depend heavily on how processes are mapped on the network. In this paper, we first analyze the impact of different process mappings on application performance on a massive Blue Gene/P system. Then, we match this analysis with application communication patterns that we allow applications to describe prior to being launched. The underlying process management system can use this combined information in conjunction with the hardware characteristics of the system to determine the best mapping for the application. Our experiments study the performance of different communication patterns, including 2D and 3D nearest-neighbor communication and structured Cartesian grid communication. Our studies, that scale up to 131,072 cores of the largest BG/P system in the United States (using 80% of the total system size), demonstrate that different process mappings can show significant difference in overall performance, especially on scale. For example, we show that this difference can be as much as 30% for P3DFFT and up to twofold for HALO. Through our proposed model, however, such differences in performance can be avoided so that the best possible performance is always achieved.

The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of using the forthcoming STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on the Cell full system simulator. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.

This paper addresses the underlying sources of performance degradation (e.g. latency, overhead, and starvation) and the difficulties of programmer productivity (e.g. explicit locality management and scheduling, performance tuning, fragmented memory, and synchronous global barriers) to dramatically enhance the broad effectiveness of parallel processing for high end computing. We are developing a hierarchical threaded virtual machine (HTVM) that defines a dynamic, multithreaded execution model and programming model, providing an architecture abstraction for HEC system software and tools development. We are working on a prototype language, LITL-X (pronounced "little-X") for Latency Intrinsic-Tolerant Language, which provides the application programmers with a powerful set of semantic constructs to organize parallel computations in a way that hides/manages latency and limits the effects of overhead. This is quite different from locality management, although the intent of both strategies is to minimize the effect of latency on the efficiency of computation. We will work on a dynamic compilation and runtime model to achieve efficient LITL-X program execution. Several adaptive optimizations will be studied. A methodology of incorporating domainspecific knowledge in program optimization will be studied. Finally, we plan to implement our method in an experimental testbed for a HEC architecture and perform a qualitative and quantitative evaluation on selected applications.

Today’s cryptanalysis on symmetric key cryptography is encouraging the use of larger key sizes and complex algorithms to achieve an unbreakable state. However, this leads an increase in computational complexity. This has promoted many researchers to develop high-performance symmetric key cryptography schemes using approaches such as the use of high-end computing hardware. Peer-to-peer (P2P) or enterprise grids are proven as one of the approaches for developing cost-effective high-end computing systems. By utilizing them, one can improve the performance of symmetric key cryptography through parallel execution. This approach makes it attractive for adoption by businesses to secure their documents. In this paper we propose and develop an application for symmetric key cryptography using enterprise grid middleware called Alchemi. An analysis and comparison of its performance is presented along with pointers to future work.

In the next section, we describe the minimal set of requirements we defined for ourselves. We then describe the Maya renderer architecture in Section 3. Section 4 describes our implementation in detail with a view on how we achieved the goals we defined for ourselves. Section 5 ...

In this paper, we propose and present the design and initial development of the Fault awareness Enabled Computing Environment (FENCE) system for high end computing. FENCE is a comprehensive fault management system in the sense that it consists of both post and runtime analysis, integrates both proactive and reactive mechanisms, and combines both application level and system level fault management. Component-based systems are also developed to support the comprehensive FENCE design. Preliminary implementation results are presented.

With the advent of Cloud computing, large-scale virtualized compute and data centers are becoming common in the computing industry. These distributed systems leverage commodity server hardware in mass quantity, similar in theory to many of the fastest Supercomputers in existence today. However these systems can consume a cities worth of power just to run idle, and require equally massive cooling systems to keep the servers within normal operating temperatures. This produces CO2 emissions and significantly contributes to the growing environmental issue of Global Warming. Green computing, a new trend for high-end computing, attempts to alleviate this problem by delivering both high performance and reduced power consumption, effectively maximizing total system efficiency.

Future high-end computers will offer great performance improvements over today's machines, enabling applications of far greater complexity. However, designers must solve the challenge of exploiting massive parallelism efficiency in the face of very high latencies across the memory hierarchy. We believe the key to meeting this challenge is the design and implementation of new models and languages which address the problems of parallelism and latency on such machines. This paper presents an overview of our ongoing research toward this goal. Specifically, we will develop a suitable program execution model, a high-level programming notation, and a compiler and runtime system based on the underlying models. These are based on our previous work in parallel multithreaded systems, but are suitably enhanced to meet the needs of future high-end computers.

The use of High Performance Computing (HPC) in commercial and consumer IT applications is becoming popular. HPC users need the ability to gain rapid and scalable access to high-end computing capabilities. Cloud computing promises to deliver such a computing infrastructure using data centers so that HPC users can access applications and data from a Cloud anywhere in the world on demand and pay based on what they use. However, the growing demand drastically increases the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high energy cost which will reduce the profit margin of Cloud providers, but also high carbon emissions which are not environmentally sustainable. Hence, there is an urgent need for energy-efficient solutions that can address the high increase in the energy consumption from the perspective of not only the Cloud provider, but also from the environment. To address this issue, we propose near-optimal scheduling policies that exploit heterogeneity across multiple data centers for a Cloud provider. We consider a number of energy efficiency factors (such as energy cost, carbon emission rate, workload, and CPU power efficiency) which change across different data centers depending on their location, architectural design, and management system. Our carbon/energy based scheduling policies are able to achieve on average up to 25% of energy savings in comparison to profit based scheduling policies leading to higher profit and less carbon emissions.

Left unchecked, the fundamental drive to increase peak performance using tens of thousands of power hungry components will lead to intolerable operating costs and failure rates. High-performance, power-aware distributed computing reduces power and energy consumption of distributed applications and systems without sacrificing performance. Recent work has shown application characteristics of single-processor, memorybound non-interactive codes and distributed, interactive web services can be exploited to conserve power and energy with minimal performance impact. Our novel approach is to exploit parallel performance inefficiencies characteristic of non-interactive, distributed scientific applications, conserving energy using DVS (dynamic voltage scaling) without impacting time-to-solution (TTS) significantly, reducing cost and improving reliability. We present a software framework to analyze and optimize distributed power-performance using DVS implemented on a 16-node Centrino-based cluster. We use our framework to quantify and compare the power-performance efficiency for parallel Fourier transform and matrix transpose codes. Using various DVS strategies we achieve application-dependent overall system energy savings as large as 25% with as little as 2% performance impact.

Adaptive mesh refinement (AMR) is a powerful technique that reduces the resources necessary to solve otherwise in-tractable problems in computational science. The AMR strategy solves the problem on a relatively coarse grid, and dynamically refines it in regions requiring higher resolution. However, AMR codes tend to be far more complicated than their uniform grid counterparts due to the software infrastructure necessary to dynamically manage the hierarchical grid framework. Despite this complexity, it is generally believed that future multi-scale applications will increasingly rely on adaptive methods to study problems at unprecedented scale and resolution. Recently, a new generation of parallel-vector architectures have become available that promise to achieve extremely high sustained performance for a wide range of applications, and are the foundation of many leadership-class computing systems worldwide. It is therefore imperative to understand the tradeoffs between conventional scalar and parallel-vector platforms for solving AMR-based calculations. In this paper, we examine the HyperCLaw AMR framework to compare and contrast performance on the Cray X1E, IBM Power3 and Power5, and SGI Altix. To the best of our knowledge, this is the first work that investigates and characterizes the performance of an AMR calculation on modern parallel-vector systems.

One critical component of future file systems for high-end computing is meta-data management. This work presents ZHT, a zero-hop distributed hash table, which has been tuned for the requirements of HEC systems. ZHT aims to be a building block for future distributed file systems to implement distributed metadata management. The goals are delivering availability, fault tolerance, high throughput, and low latencies. ZHT has some important properties, such as being lightweight , fault tolerant using replication and persistence. We have evaluated ZHT's performance under a variety of systems, ranging from a Linux cluster to an IBM BlueGene/P supercomputer. We scaled ZHT up to 16K processes and achieved 4M operations/sec throughput. Latencies have scaled similarly well, with sub-milliseconds latencies at 4K-core scales. We compared ZHT against other systems and found it offers superior performance for the features and portability it supports.

Today’s cryptanalysis on symmetric key cryptography is encouraging the use of larger key sizes and complex algorithms to achieve an unbreakable state. However, this leads an increase in computational complexity. This has promoted many researchers to develop high-performance symmetric key cryptography schemes using approaches such as the use of high-end computing hardware. Peer-to-peer (P2P) or enterprise grids are proven as one of the approaches for developing cost-effective high-end computing systems. By utilizing them, one can improve the performance of symmetric key cryptography through parallel execution. This approach makes it attractive for adoption by businesses to secure their documents. In this paper we propose and develop an application for symmetric key cryptography using enterprise grid middleware called Alchemi. An analysis and comparison of its performance is presented along with pointers to future work.

Achieving good performance on high-end computing systems is growing ever more challenging due to enormous scale, increasing architectural complexity, and increasing application complexity. To address these challenges in DOE's SciDAC-2 program [1] , the Performance Engineering Research Institute (PERI) has embarked on an ambitious research plan encompassing performance modeling and prediction, automatic performance optimization and performance engineering of high profile applications. The principal new component is a research activity in automatic tuning software, which is spurred by the strong user preference for automatic tools.

Parallel I/O is fast becoming a bottleneck to the research agendas of many users of extreme scale parallel computers. The principle cause of this is the concurrency explosion of high-end computation, coupled with the complexity of providing parallel file systems that perform reliably at such scales. More than just being a bottleneck, parallel I/O performance at scale is notoriously variable, being influenced by numerous factors inside and outside the application, thus making it extremely difficult to isolate cause and effect for performance events. In this paper, we propose a statistical approach to understanding I/O performance that moves from the analysis of performance events to the exploration of performance ensembles. Using this methodology, we examine two I/O-intensive scientific computations from cosmology and climate science, and demonstrate that our approach can identify application and middleware performance deficiencies -resulting in more than 4× run time improvement for both examined applications.

Today's cryptanalysis on symmetric key cryptography is encouraging the use of larger key sizes and complex algorithms to achieve an unbreakable state. However, this leads an increase in computational complexity. This has promoted many researchers to develop high-performance symmetric key cryptography schemes using approaches such as the use of high-end computing hardware. Peer-to-peer (P2P) or enterprise grids are proven as one of the approaches for developing cost-effective high-end computing systems. By utilizing them, one can improve the performance of symmetric key cryptography through parallel execution. This approach makes it attractive for adoption by businesses to secure their documents. In this paper we propose and develop an application for symmetric key cryptography using enterprise grid middleware called Alchemi. An analysis and comparison of its performance is presented along with pointers to future work.

Failure management is crucial for high performance computing systems, especially when the complexity of applications and underlying infrastructure has grown sharply in recent years. In this paper, we present the design, implementation and experiment of Trouble Dashboard (TD), an adaptive, flexible, and low overhead failure monitoring system. Our goal is to provide a lightweight, scalable failure-monitoring tool for both application scientists and system managers. In TD, a set of APIs is provided for application scientists to control the behavior of their applications with flexibility when failures happen. System managers can use the tool to monitor the status of not only computing nodes and running tasks but also failures when they occur. Experiments show that TD incurs low overhead, and remains accurate and flexible enough to adapt to various applications.

This white paper addresses three separate questions in the HECRTF call for white papers: (2d) performance metrics that quantify benefits; (5) practical performance measures for system procurement that correlate well with realized performance of actual applications; and, (6) methods for deriving system performance targets from actual or projected application requirements or other user needs.

This paper proposes the study of a new computation model that attempts to address the underlying sources of performance degradation (e.g. latency, overhead, and starvation) and the difficulties of programmer productivity (e.g. explicit locality management and scheduling, performance tuning, fragmented memory, and synchronous global barriers) to dramatically enhance the broad effectiveness of parallel processing for high end computing. In this paper, we present the progress of our research on a parallel programming and execution model-mainly, ParalleX. We describe the functional elements of ParalleX, one such model being explored as part of this project. We also report our progress on the development and study of a subset of ParalleX-the LITL-X at University of Delaware. We then present a novel architecture model-Gilgamesh II-as a ParalleX processing architecture. A design point study of Gilgamesh II and the architecture concept strategy are presented.

As the power consumption of a server system becomes a mainstream concern in enterprise environments, understanding the system's power behavior at varying utilization levels provides us a key to select appropriate energy-efficiency optimizations. In this work, we present an in-depth analysis of 177 SPECpower ssj2008 results published between 2007-2010 to understand the changes of server's power behavior over time. In particular, we identified simple nonlinear functions appropriate for modeling the power behavior of today's, aggressively power-managed, machines. We consider this work as an important first step towards developing capability for power signature analysis of a high-end computer system.

Coordinated Observation and Prediction of the Earth System (COPES) 3 , the WCRP is embarking on an ambitious, decade-long observing and modeling activity that is intended to improve understanding of the mechanisms that determine the mean climate and its variability, with the ultimate objective of providing the soundest possible scientific basis for a predictive capability for the total climate system. The framework calls for an integrated approach in which the roles of the atmosphere, ocean, land and cryosphere are considered in comprehensive models of the climate system, which are also capable of assimilating weather and climate observations. The continuum of prediction problems, from weatherto-climate and days-to-decades, will be addressed by a hierarchy of models that should become increasingly similar to one another, merging eventually into "unified models" with common infrastructure and interchangeable parameterizations, and variously configured to address a wide range of problems. This fact, when considered with the anticipated increasing international coordination of model development, integration and analysis, implies that the modeling and model output data management challenges will be very large. The COPES framework calls for global models to be run with "resolution of a few kilometers (as required for many practical applications), very large model ensembles to assess uncertainty, simulations of paleoclimates with fully coupled global climate models, and highly-resolved regional models in response to the demand to develop adaptation policies and measures at the regional level." It also calls for high-resolution models that include detailed parameterizations, cloud-systemresolving capability, and other detailed representation of relevant climate processes. The increasing resolution, complexity, ensemble size and run duration of these climate prediction systems represent a fundamental challenge to the evolving capability of high-end computing. This white paper is intended to raise several issues of relevance to high-end computing for COPES. The white paper attempts to summarize the technology trends and model computing requirements for the next decade, and to make recommendations for how to proceed. Weather-to-Climate, Days-to-Decades Modeling The modeling systems likely to be used to realize the COPES vision of weather-to-climate, days-to-decades prediction will be highly sophisticated computer programs that represent the coupled

1] It is known that General Circulation Models (GCMs) have insufficient resolution to accurately simulate hurricane near-eye structure and intensity. The increasing capabilities of high-end computers have changed this. The mesoscaleresolving finite-volume GCM (fvGCM) has been experimentally deployed on the NASA Columbia supercomputer, and its performance is evaluated in this study by choosing hurricane Katrina as an example. In late August 2005, Katrina underwent two stages of rapid intensification, and became the sixth most intense hurricane in the Atlantic. Six 5-day simulations of Katrina at both 0.25°and 0.125°show comparable track forecasts but the 0.125°runs provide much better intensity forecasts, producing the center pressure with errors of only ±12 hPa. In the runs examined in this study, the 0.125°simulates better near-eye wind distributions and a more realistic average intensification rate. To contribute to the ongoing research on the effects of disabling convection parameterization (CP), we present promising results by comparing 0.125°runs with disabled CPs against runs with enabled CPs.

In this work we present an scientific application that has been given a Hadoop MapReduce implementation. We also discuss other scientific fields of supercomputing that could benefit from a MapReduce implementation. We recognize in this work that Hadoop has potential benefit for more applications than simply datamining, but that it is not a panacea for all data intensive applications.

The rapid growth of InfiniBand, 10 Gigabit Ethernet/iWARP and IB WAN extensions is increasingly gaining momentum for designing high end computing clusters and data-centers. For typical applications such as data staging, content replication and remote site backup etc., FTP has been the most popular method to transfer bulk data within and across these clusters or data-centers. Although the existing sockets based FTP approaches can be transparently used in these systems through the protocols like IPoIB or SDP, their performance and scalability are limited due to the additional interaction overhead and unoptimized protocol processing. This leads to a challenge how to design more efficient FTP mechanisms by leveraging the advanced features of modern interconnects.

1] The increasing capability of high-end computers allows numerical simulations with horizontal resolutions high enough to resolve cloud systems in a global model. In this paper, initial results from the global Nonhydrostatic ICosahedral Atmospheric Model (NICAM) are highlighted to demonstrate the beginning of a potentially new era for weather and climate predictions with global cloud-systemresolving models. The NICAM simulation with a horizontal resolution of about 7 km successfully reproduced the lifecycles of two real tropical cyclones that formed in Indian Ocean in the austral summer 2006. Initialized with the atmospheric conditions 1-2 weeks before the cyclones genesis, the model captured reasonably not only the timing of the observed cyclone geneses but also their motions and mesoscale structures. The model provides a high temporal/spatial resolution dataset for detailed studies of mesoscale aspects of tropical cyclone genesis. These promising results suggest the predictability of tropical cyclones by high-resolution global cloud-system-resolving models. Citation: Fudeyasu, H., Y. Wang, M. Satoh, T. Nasuno, H. Miura, and W. Yanase (2008), Global cloud-system-resolving model NICAM successfully simulated the lifecycles of two real tropical cyclones, Geophys. Res. Lett., 35, L22808,

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 computing 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. * Employee of Computer Sciences Corporation.

The last decade has witnessed a rapid proliferation of superscalar cache-based microprocessors to build high-end computing (HEC) platforms, primarily because of their generality, scalability, and cost effectiveness. However, the growing gap between sustained and peak performance for full-scale scientific applications on conventional supercomputers has become a major concern in high per-

AQUAGRID is the subsurface hydrology computational service of the Sardinian GRIDA3 infrastructure, designed to deliver complex environmental applications via a user-friendly Web portal. The service aims to provide to water professionals integrated modeling tools to solve water resources management problems and aid decision making for contaminated soil and groundwater. In this paper, the AQUAGRID application concept and enabling technologies are illustrated. At the heart of the service are the computational models to simulate complex and large groundwater flow and contaminant transport problems and geochemical speciation. AQUAGRID is built on top of compute-Grid technologies by means of the EnginFrame Grid framework. Distributed data management is provided by the Storage Resource Broker data-Grid middleware. The resulting environment allows end-users to perform groundwater simulations and to visualize and interact with their results, using graphs, 3D images and annotated maps. The problem solving capability of the platform is demonstrated using the results of two case studies deployed.

This document proposes a multi-site strategy for creating a new class of computing capability for the U.S. by undertaking the research and development necessary to build supercomputers optimized for science in partnership with the American computer industry.

In order to take full advantage of high-end computing platforms, scientific applications often require modifications to source codes, and to their build systems that generate executable files. The ever-increasing emphasis on productivity in HPC makes the efficiency of the build process an extremely important issue. Our work is motivated by the need for a systematic approach to the HPC lifecycle that encompasses build and porting tasks. In this paper we briefly present the design of the Harness Workbench Toolkit (HWT) that addresses the porting issue through a user-assisted conversion process. We briefly describe our adaptation capability model that includes routine conversions (string substitutions) as well as more advanced transformations such as 32-64 bit changes. Next, we show and discuss results of an experiment with a production source code (the CPMD application) that examines the effort of adapting the baseline code (the Linux distribution) to specific high-end machines (IBM SP4, Cray X1E, Cray XT3/4) in terms of the number of necessary conversions. Based on the conversion capability model, we have implemented conversion assistant modules that were used in the experiment. The experimental results are promising and demonstrate that our approach takes a step towards improving the overall productivity of scientific applications on high-end machines.

Linux clusters have become very popular for scientific com- puting at research institutions world-wide, because they can be easily deployed at a fairly low cost. However, the most pressing issues of today's cluster solutions are availability and serviceability. The conventional Beowulf cluster archi- tecture has a single head node connected to a group of com- pute nodes. This head node

Maya is the new 3D software package recently released by Alias|Wavefront for creating state-of-the-art character animation and visual effects. Built on a nextgeneration advanced architecture, Maya delivers high speed interaction and high productivity for its users. In the Fall of 1995, the Rendering Team at Alias|Wavefront started from scratch to design and implement a renderer for the Maya project. This was a very challenging task, requiring the efficient generation of high-quality images for a next-generation 3D application that was still under development. In addition, we were expected to match or exceed the capabilities of our existing popular rendering products (as well as those from our competitors). In January of 1998, the all-new renderer was delivered with Maya 1.0. It includes a comprehensive user interface that is well integrated with the rest of the system, and a batch renderer that is capable of efficiently generating a full spectrum of high-quality visual effects. Currently, there are high-end computer graphics (CG) productions in progress that are using the Maya renderer. In this paper, we will concentrate on our batch renderer implementation effort. We will describe the philosophy, design decisions, and the tasks we set out to achieve in 1995. We will then evaluate the delivered system based on images generated with the renderer. ® Registered trademark of Alias|Wavefront, a division of Silicon Graphics Limited.

Abstract. The OptIPuter is a radical distributed visualization, teleimmersion, data mining and computing architecture. Observing that the exponential growth rates in bandwidth and storage are now much higher than Moore's Law, this major new project of several US universities in Southern California and Illinois “goes to the end of the rainbow” to exploit a new world in which the central architectural element is optical networking, not computers. This transition is caused by the use of parallelism, as in supercomputing a decade ago. ...

In 2003, the High End Computing Revitalization Task Force designated file systems and I/O as an area in need of national focus. The purpose of the High End Computing Interagency Working Group (HECIWG) is to coordinate government spending on File Systems and I/O (FSIO) R&D by all the government agencies that are involved in High End Computing. The HECIWG tasked a smaller advisory group to list, categorize, and prioritize HEC I/O and File Systems R&D needs. In 2005, leaders in FSIO from academia, industry and government agencies collaborated to list and prioritize areas of research in HEC FSIO. This led to a very successful High End Computing University Research Activity (HECURA) call from NSF in 2006 and has prompted a new HECURA call from NSF in 2009. This paper serves as both a review of the 2008 HEC FSIO identified research gaps as well as a preview of this forthcoming HECURA call.