With the increase of complex scientific simulations driven by workflows and heterogeneous workload profiles, managing system resources effectively is essential for improving performance and system throughput, especially due to trends like heterogeneous HPC and deeply integrated systems with on-chip accelerators. For optimal resource utilization, dynamic resource allocation can improve productivity across all system and application levels, by adapting the applications' configurations to the system's resources. In this context, malleable jobs, which can change resources at runtime, can increase the system throughput and resource utilization while bringing various advantages for HPC users (e.g., shorter waiting time). Malleability has received much attention recently, even though it has been an active research area for almost two decades [1]. This paper presents the state-of-the-art of malleable implementations in HPC systems, targeting mainly malleability in compute and I/O resources. Based on our experiences, we state our current concerns and list future opportunities for research.

Abstract Financial Monte Carlo simulations are computationally intensive applications that must meet tight deadlines in terms of job completion times. The completion time might have a huge impact on the financial profits made from decisions derived from the simulation results. Naturally, there is a huge interest in being able to simulate as fast as possible. While single simulations can be done on one machine, decisions often depend on portfolios of simulations. Distributing the workload among resources is crucial to achieve low latency. In this article we present a combination of a middleware with a high‐performance implementation of an Asian options evaluation code on the Cell Broadband Engine (CBE). We handle workload distribution with our PHASTGrid middleware and provide users with a web service interface to the whole infrastructure. The CBE is particularly suitable for Monte Carlo simulations. We implemented a well‐known algorithm on both the CBE and the Intel x86 multicore architectures. Both codes are integrated in our middleware, allowing a direct comparison of the performance and scalability. In addition to the Monte Carlo simulation, we also use different applications and compare our middleware with Globus. Copyright © 2009 John Wiley & Sons, Ltd.