Jean Luca Bez

Performance and energy efficiency analysis of HPC physics simulation applications in a cluster of ARM processors

We analyze the feasibility and energy efficiency of using an unconventional cluster of low‐power Advanced RISC Machines processors to execute two scientific parallel applications. For this purpose, we have selected two applications that present high computational and communication cost: the Ondes3D that simulates geophysical events, and the all‐pairs N‐Body that simulates astrophysical events. We compare and discuss the impact of different compilation directives and processor frequency and how they interfere in Time‐to‐Solution and Energy‐to‐Solution. Our results demonstrate that by correctly tuning the application at compile time, for the Advanced RISC Machines architecture, we can considerably reduce the execution time and the energy spent by computing simulations. Furthermore, we observe reductions of up to 54.14% in Time‐to‐Solution and gains of up to 53.65% in Energy‐to‐Solution with two cores. Additionally, we consider the impact of two processor frequency governors on these metrics. Results indicate that the powersave governor presents a smaller instantaneous power consumption. However, it spends more time executing tasks, increasing the energy needed to achieve the solution. Finally, we correlate the energy consumption with the execution time in the experimental results using Pareto. These findings suggest that it is possible to explore low‐powered clusters for high‐performance computing applications by tuning application and hardware configuration to achieve energy efficiency. Copyright

URI Online Judge Academic: A tool for algorithms and programming classes

The URI Online Judge Academic is an online tool that assists professors in programming classes and motivates students to practice more and to go beyond the theoretical base learned in class and, as a result, helps them sharpen their logical, algorithmically and programming skills. The Academic module enables professors to manage disciplines and lists of exercises on specific programming topics in a visual and organized online environment that presents several benefits compared to the traditional method of handwritten lists. The purpose of this article is to introduce the features and the benefits available in version 3.0 of the tool.

Towards energy-efficient storage servers

As large-scale parallel platforms are deployed to comply with the increasing performance requirements of scientific applications, a new concern is getting the attention of the HPC community: the power consumption. In this paper, we aim at evaluating the viability of using low-power architectures as file systems servers in HPC environments, since processing power is of less importance for these servers. We present a performance and energy efficiency study of such low-power servers when compared to conventional architectures. Our results indicate that the low-power alternative could be a viable choice to save energy by up to 85+ while not compromising on performance, specially for read-intensive workloads. We show the low-power server provides 7 times more energy efficiency to the system while running a real application from the seismic wave propagation field.

Energy efficiency and I/O performance of low-power architectures: Energy efficiency and I/O performance of low-power architectures

This paper presents an energy efficiency and I/O performance analysis of low‐power architectures when compared to conventional architectures, with the goal of studying the viability of using them as storage servers. Our results show that despite the fact the power demand of the storage device amounts for a small fraction of the power demand of the whole system, significant increases in power demand are observed when accessing the storage device. We investigate the access pattern impact on power demand, looking at the whole system and at the storage device by itself, and compare all tested configurations regarding energy efficiency. Then we extrapolate the conclusions from this research to provide guidelines for when considering the replacement of traditional storage servers by low‐power alternatives. We show the choice depends on the expected workload, estimates of power demand of the systems, and factors limiting performance. These guidelines can be applied for other architectures than the ones used in this work.

A Checkpoint of Research on Parallel I/O for High-Performance Computing

We present a comprehensive survey on parallel I/O in the high-performance computing (HPC) context. This is an important field for HPC because of the historic gap between processing power and storage latency, which causes application performance to be impaired when accessing or generating large amounts of data. As the available processing power and amount of data increase, I/O remains a central issue for the scientific community. In this survey article, we focus on a traditional I/O stack, with a POSIX parallel file system. We present background concepts everyone could benefit from. Moreover, through the comprehensive study of publications from the most important conferences and journals in a 5-year time window, we discuss the state of the art in I/O optimization approaches, access pattern extraction techniques, and performance modeling, in addition to general aspects of parallel I/O research. With this approach, we aim at identifying the general characteristics of the field and the main current and future research topics.