SUMMARY Unconventional multi‐ and many‐core processors (e.g. IBM ® Cell B.E. TM and NVIDIA ® GPU) have emerged as effective accelerators in trial climate and weather simulations. Yet these climate and weather models typically run on parallel computers with conventional processors (e.g. Intel ®, AMD ®, and IBM) using Message Passing Interface. To address challenges involved in efficiently and easily connecting accelerators to parallel computers, we investigated using IBM's Dynamic Application Virtualization TM (IBM DAV) software in a prototype hybrid computing system with representative climate and weather model components. The hybrid system comprises two Intel blades and two IBM QS22 Cell B.E. blades, connected with both InfiniBand ® (IB) and 1‐Gigabit Ethernet. The system significantly accelerates a solar radiation model component by offloading compute‐intensive calculations to the Cell blades. Systematic tests show that IBM DAV can seamlessly offload compute‐intensive calculations from Intel blades to Cell B.E. blades in a scalable, load‐balanced manner. However, noticeable communication overhead was observed, mainly due to IP over the IB protocol. Full utilization of IB Sockets Direct Protocol and the lower latency production version of IBM DAV will reduce this overhead. Copyright © 2011 John Wiley & Sons, Ltd.

Unconventional multi- and many-core processors (e.g., IBM® Cell B.E. ™ and NVIDIA® GPU) have emerged as effective accelerators in trial climate and weather simulations. Yet these climate and weather models typically run on parallel computers with conventional processors (e.g., Intel®, AMD®, and IBM) using Message Passing Interface (MPI). To address challenges involved in efficiently and easily connecting accelerators to parallel computers, we investigated using IBM's Dynamic Application Virtualization (DAV) software in a prototype hybrid computing system with representative climate and weather model components. The hybrid system comprises 2 Intel blades and 2 IBM QS22 Cell B.E. blades, connected with both InfiniBand® (IB) and 1-Gigabit Ethernet. The system significantly accelerates a solar radiation model component by offloading compute-intensive calculations to the Cell blades. Systematic tests show that DAV can seamlessly offload compute-intensive calculations from Intel blades to Cell B.E. blades in a scalable, load-balanced manner. However, noticeable communication overhead was observed, mainly due to IP over IB protocol. Full utilization of IB Sockets Direct Protocol (SDP) and the lower latency production version of DAV will reduce this overhead.

Cloud computing is emerging as an alternative computing platform to bridge the gap between scientists' growing computational demands and their computing capabilities. A scientist who wants to run HPC applications can obtain massive computing resources 'in the cloud' quickly (in minutes), as opposed to days or weeks it normally takes under traditional business processes. Due to the popularity of Amazon EC2, most HPC-in-the-cloud research has been conducted using EC2 as a target platform. Previous work has not investigated how results might depend upon the cloud platform used. In this paper, we extend previous research to three public cloud computing platforms. In addition to running classical benchmarks, we also port a 'full-size' NASA climate prediction application into the cloud, and compare our results with that from dedicated HPC systems. Our results show that 1) virtualization technology, which is widely used by cloud computing, adds little performance overhead; 2) most current public clouds are not designed for running scientific applications primarily due to their poor networking capabilities. However, a cloud with moderately better network (vs. EC2) will deliver a significant performance improvement. Our observations will help to quantify the improvement of using fast networks for running HPC-in-the-cloud, and indicate a promising trend of HPC capability in future private science clouds. We also discuss techniques that will help scientists to best utilize public cloud platforms despite current deficiencies.

Abstract The call for ever‐increasing model resolutions and physical processes in climate and weather models demands a continual increase in computing power. The IBM Cell processor's order‐of‐magnitude peak performance increase over conventional processors makes it very attractive to fulfill this requirement. However, the Cell's characteristics, 256 kB local memory per SPE and the new low‐level communication mechanism, make it very challenging to port an application. As a trial, we selected the solar radiation component of the NASA GEOS‐5 climate model, which: (1) is representative of column‐physics components (half of the total computational time), (2) has an extremely high computational intensity: the ratio of computational load to main memory transfers, and (3) exhibits embarrassingly parallel column computations. In this paper, we converted the baseline code (single‐precision Fortran) to C and ported it to an IBM BladeCenter QS20. For performance, we manually SIMDize four independent columns and include several unrolling optimizations. Our results show that when compared with the baseline implementation running on one core of Intel's Xeon Woodcrest, Dempsey, and Itanium2, the Cell is approximately 8.8x, 11.6x, and 12.8x faster, respectively. Our preliminary analysis shows that the Cell can also accelerate the dynamics component (∼25% total computational time). We believe these dramatic performance improvements make the Cell processor very competitive as an accelerator. Copyright © 2009 John Wiley & Sons, Ltd.

NASA Earth and space science applications are currently utilizing geographically distributed computational platforms. These codes typically require the most compute cycles and generate the largest amount of data over any other applications currently supported by NASA. Furthermore, with the development of a leadership class SGI system at NASA Ames (Project Columbia), NASA has created an agency wide computational resource. This resource is heavily employed by Earth and space science users resulting in large amounts of data. The management of this data in a distributed environment requires a significant amount of effort from the users. This paper defines the approach taken to create an enabling infrastructure to help users easily access and move data across distributed computational resources. Specifically, this paper discusses the approach taken to create a wide area storage area network (SAN) using the SGI CXFS file system over standard TCP/IP. In addition, an emerging technology test bed initiative is described to show how NASA is creating an environment to continually evaluate new technology for data intensive computing.

User identifiers/passwords are an integral part of the first line of defence of a computer system. Ideally, each user should have a unique logon assigned to him or her but because of work demands, the practice of sharing logons in user groups is now emerging. A study of security officers in a large Australian organization examined the security awareness of these officers and the reasons for, and current management practices of, shared logons. It was found that work flow efficiency was more important than access control and that policies for user group access control are urgently needed.