Correlated Resource Models of Internet End Hosts

Understanding and modelling resources of Internet end hosts is essential for the design of desktop software and Internet-distributed applications. In this paper we develop a correlated resource model of Internet end hosts based on real trace data taken from the SETI@home project. This data covers a 5-year period with statistics for 2.7 million hosts. The resource model is based on statistical analysis of host computational power, memory, and storage as well as how these resources change over time and the correlations between them. We find that resources with few discrete values (core count, memory) are well modeled by exponential laws governing the change of relative resource quantities over time. Resources with a continuous range of values are well modeled with either correlated normal distributions (processor speed for integer operations and floating point operations) or log-normal distributions (available disk space). We validate and show the utility of the models by applying them to a resource allocation problem for Internet-distributed applications, and demonstrate their value over other models. We also make our trace data and tool for automatically generating realistic Internet end hosts publicly available

Analysis, Tracing, Characterization and Performance Modeling of Select ASCI Applications for BlueGene/L Using Parallel Discrete Event Simulation

Caltech's Jet Propulsion Laboratory (JPL) and Center for Advanced Computer Architecture (CACR) are conducting application and simulation analyses of Blue Gene/L[1] in order to establish a range of effectiveness of the architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution

Performance analysis of direct N-body algorithms for astrophysical simulations on distributed systems

We discuss the performance of direct summation codes used in the simulation of astrophysical stellar systems on highly distributed architectures. These codes compute the gravitational interaction among stars in an exact way and have an O(N^2) scaling with the number of particles. They can be applied to a variety of astrophysical problems, like the evolution of star clusters, the dynamics of black holes, the formation of planetary systems, and cosmological simulations. The simulation of realistic star clusters with sufficiently high accuracy cannot be performed on a single workstation but may be possible on parallel computers or grids. We have implemented two parallel schemes for a direct N-body code and we study their performance on general purpose parallel computers and large computational grids. We present the results of timing analyzes conducted on the different architectures and compare them with the predictions from theoretical models. We conclude that the simulation of star clusters with up to a million particles will be possible on large distributed computers in the next decade. Simulating entire galaxies however will in addition require new hybrid methods to speedup the calculation.Comment: 22 pages, 8 figures, accepted for publication in Parallel Computin

Revisiting Matrix Product on Master-Worker Platforms

This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous master-worker platforms. While matrix-product is well-understood for homogeneous 2D-arrays of processors (e.g., Cannon algorithm and ScaLAPACK outer product algorithm), there are three key hypotheses that render our work original and innovative: - Centralized data. We assume that all matrix files originate from, and must be returned to, the master. - Heterogeneous star-shaped platforms. We target fully heterogeneous platforms, where computational resources have different computing powers. - Limited memory. Because we investigate the parallelization of large problems, we cannot assume that full matrix panels can be stored in the worker memories and re-used for subsequent updates (as in ScaLAPACK). We have devised efficient algorithms for resource selection (deciding which workers to enroll) and communication ordering (both for input and result messages), and we report a set of numerical experiments on various platforms at Ecole Normale Superieure de Lyon and the University of Tennessee. However, we point out that in this first version of the report, experiments are limited to homogeneous platforms