3,294 research outputs found
Iso-energy-efficiency: An approach to power-constrained parallel computation
Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
A MIDDLE-WARE LEVEL CLIENT CACHE FOR A HIGH PERFORMANCE COMPUTING I/O SIMULATOR
This thesis describes the design and run time analysis of the system level middle-ware cache for Hecios. Hecios is a high performance cluster I/O simulator. With Hecios, we provide a simulation environment that accurately captures the performance characteristics of all the components in a clusterwide parallel file system. Hecios was specifically modeled after PVFS2. It was designed to be extensible and to easily allow for various component modules to be easily replaced by those that model other system types. Built around the OMNeT++ simulation package, Hecios\u27 inner-cluster communication module is easily adaptable to any TCP/IP based protocol and all standard network interface cards, switches, hubs, and routers. We will examine the system cache component and describe a methodology for implementing other coherence and replacement techniques within Hecios. Similar to other cache simulation tools, we allow the size of the system cache to be varied independently of the replacement policy and caching technique used
From access and integration to mining of secure genomic data sets across the grid
The UK Department of Trade and Industry (DTI) funded BRIDGES project (Biomedical Research Informatics Delivered by Grid Enabled Services) has developed a Grid infrastructure to support cardiovascular research. This includes the provision of a compute Grid and a data Grid infrastructure with security at its heart. In this paper we focus on the BRIDGES data Grid. A primary aim of the BRIDGES data Grid is to help control the complexity in access to and integration of a myriad of genomic data sets through simple Grid based tools. We outline these tools, how they are delivered to the end user scientists. We also describe how these tools are to be extended in the BBSRC funded Grid Enabled Microarray Expression Profile Search (GEMEPS) to support a richer vocabulary of search capabilities to support mining of microarray data sets. As with BRIDGES, fine grain Grid security underpins GEMEPS
Exploring Application Performance on Emerging Hybrid-Memory Supercomputers
Next-generation supercomputers will feature more hierarchical and
heterogeneous memory systems with different memory technologies working
side-by-side. A critical question is whether at large scale existing HPC
applications and emerging data-analytics workloads will have performance
improvement or degradation on these systems. We propose a systematic and fair
methodology to identify the trend of application performance on emerging
hybrid-memory systems. We model the memory system of next-generation
supercomputers as a combination of "fast" and "slow" memories. We then analyze
performance and dynamic execution characteristics of a variety of workloads,
from traditional scientific applications to emerging data analytics to compare
traditional and hybrid-memory systems. Our results show that data analytics
applications can clearly benefit from the new system design, especially at
large scale. Moreover, hybrid-memory systems do not penalize traditional
scientific applications, which may also show performance improvement.Comment: 18th International Conference on High Performance Computing and
Communications, IEEE, 201
Performance analysis of a database caching system in a grid environment
Tese de mestrado. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 200
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