Cloud computing is now a mainstream technology in many application domains and user constituencies. For scientific high-performance applications in academic and research settings however, the trade-offs between cost and elasticity on the one hand, and performance and access on the other, is not always clear. We discuss our experiences with comparing cost and utility for a hemodynamics computational fluid dynamics code on three typical platform-types available to researchers: IaaS clouds, grids, and on-premise local resources. To rank the tested platforms, we introduce a simple utility function describing the value of a completed computational task to the user as a function of the wait time and the cost of the computation. Our results suggest that IaaS clouds can be a convenient choice for the considered class of CFD simulations, providing a valuable trade-off between cost and task completion time.

High-performance applications are often developed for a specific class of target platforms and executing them on the increasing variety of Cloud and grid environments requires substantial adjustments and reconciliation. The aim of our framework, called ADAPT (Adaptive Application and Platform Translation), is to allow adaptation of applications for execution on various computational resources. The overall objective is to enhance usability of cyber-infrastructure platforms by providing automated adaptations of applications and conditioning of target environments so that greater cross-utilization is achieved. This paper presents a proof-of-concept experiment of a possible use of ADAPT, viz. executing a C/MPI application originally written for clusters on the Microsoft Azure infrastructure. This MPI application-to-cloud adjustment is based on automatic identification of the application programming paradigm followed by applying an application transformation to enable execution on an alternative target.

Our on-going project, Unibus, aims to facilitate provisioning and aggregation of multifaceted resources from resource providers and end-users' perspectives. To achieve that, Unibus proposes (1) the Capability Model and mediators (resource drivers) to virtualize access to diverse resources, and (2) soft and successive conditioning to enable automatic and user-transparent resource provisioning. In this paper we examine the Unibus concepts and prototype in a real situation of aggregation of two commercial clouds and execution of benchmarks on aggregated resources. We also present and discuss benchmarks' results.

High-end machines at modern HPC centers are constantly undergoing hardware and system software upgrades - necessitating frequent rebuilds of application codes. The number of possible combinations of compilers, libraries, application build configurations, differing hardware architectures, etc, makes the process of building applications very onerous, requiring expert build knowledge from different domains. Our ongoing Harness Workbench Toolkit (HWT) project aims to foster and streamline the entire build process on heterogeneous computational platforms. This paper focuses on a key research issue of the HWT that regards facilitating and enhancement portability of build systems across multifarious machines, with particular respect to scientific software commonly used in the HPC community. The article presents a novel HWT approach based on the concept of generic build systems and profiles which encapsulate build knowledge provided independently by relevant experts. The paper describes profiles, the logistics of storing and retrieving build information, and interfacing to user-guided builds. We also report on experiences with applying the HWT approach to two scientific production codes (CPMD, GAMESS) on Cray XT4.

Fast hardware turnover in supercomputing centers, stimulated by rapid technological progress, results in high heterogeneity among HPC platforms, and necessitates that applications are ported and adapted frequently. The cutting-edge nature of the hardware mandates customized performance tuning, which, coupled with continuously growing application complexity, makes the process inherently and increasingly challenging. In this paper, we analyze build procedures of a representative set of HPC applications, and attempt to identify commonalities that can be exploited to enhance cross-platform portability. We then propose a novel method for reducing non-portabilities while preserving high performance. The approach, based on profiles that capture and isolate non-portable features at various levels, requires only a moderate amount of changes to existing makefiles. It leverages the expertise of system designers and administrators, and reduces burdens placed on application scientists. As a proof of concept, we discuss the application of our methodology to enhancing portability of the Mile application across heterogeneous HPC platforms.

Shared HPC platforms continue to require substantial effort for software installation and management, often necessitating manual intervention and tedious procedures. We propose a novel model of resource sharing that shifts resource virtualization and aggregation responsibilities to client-side software, thus reducing the burdens on resource providers.The Zero-Force MPI toolkit automates the installation, build, run, and post-processing stages of HPC applications, thus allowing application scientists to focus on using resources instead of managing them. Through a provided console, MPI runtime systems, support libraries, application executables, and needed datafiles can be soft-installed across distributed resources with just a few commands. Built-in data synchronization capabilities simplify common HPC development tasks, saving end-user time and effort. To evaluate ZF-MPI, we conducted experiments with the NAS Parallel Benchmarks. Results demonstrate that the proposed run-not-install approach is effective and may substantially increase overall productivity.