IT-based scientific research requires high computational resources. The limitaton on funding and infrastructure led the high performance computing era from supercomputer to cluster and grid computing technology. Parallel application running well on cluster computer as well as supercomputer, one of the type is embarassingly parallel application. Many scientist loves EP because it doesn’t need an...

We describe the key role played by partial evaluation in the Supercomputer Toolkit, a parallel computing system for scienti c applications that e ectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputer Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at M.I.T., and have made possibl...

The performance of supercomputer schedulers is influenced by the workloads that serve as their input. Realistic workloads are therefore critical to evaluate how supercomputer schedulers perform in practice. There has been much written in the literature about rigid parallel jobs, i.e. jobs that require partitions of a fixed size to run. However the majority of the parallel jobs in production tod...

This paper presents an analysis of utilizing unused cycles on supercomputers through the use of many small jobs. What we call “interstitial computing,” is important to supercomputer centers for both productivity and political reasons. Interstitial computing makes use of the fact that small jobs are more or less fungible consumers of compute cycles that are more efficient for bin packing than th...

Grids are becoming mission-critical components in research and industry, offering sophisticated solutions in leveraging largescale computing and storage resources. Grid resources are usually shared among multiple organizations in an opportunistic manner. However, an opportunistic or “best effort” quality-of-service scheme may be inadequate in situations where a large number of resources need to...

In the past four years cloud computing has emerged as an alternative platform for high performance computing. Unfortunately, there is still confusion about the cloud model and its advantages and disadvantages over tradition supercomputing based problem solving methods. In this note we characterize the ways in which cloud computing can be used productively in scientific and technical application...

38 Computer P u b l i s h e d b y t h e I E E E C o m p u t e r S o c i e t y puters too unreliable for application scientists to use. Unfortunately, building exotic cooling facilities can cost as much as the supercomputer itself, and operating and maintaining the facilities costs even more. As “The Energy-Efficient Green Destiny” sidebar details, the low-power supercomputer that we developed w...

The aim of our work is to investigate the performance and overall scalability of an optimistic discrete-event simulator on a Blue Gene/L supercomputer. We find that strong scaling out to 16,384 processors is possible. In terms of event-rate, we observed 853 million events per second on 16,384 processors for the PHOLD benchmark. This is 1.5 times faster than any previously reported PDES synchron...

The architecture of the BlueGene/L massively parallel supercomputer is described. Each computing node consists of a single compute ASIC plus 256 MB of external memory. The compute ASIC integrates two 700 MHz PowerPC 440 integer CPU cores, two 2.8 Gflops floating point units, 4 MB of embedded DRAM as cache, a memory controller for external memory, six 1.4 Gbit/s bi-directional ports for a 3-dime...

This paper describes an effort at UC Berkeley and the International Computer Science Institute to develop a supercomputer for artificial neural network applications. Our perspective has been strongly influenced by earlier experiences with the construction and use of a simpler machine. In particular, we have observed Amdahl's Law in action in our designs and those of others. These observations i...