Richard A. Swetz

System management in the BlueGene/L supercomputer

The BlueGene/L supercomputer will use system-on-a-chip integration and a highly scalable cellular architecture to deliver 360 teraflops of peak computing power. With 65536 compute nodes, BlueGene/L represents a new level of scalability for parallel systems. As such, it is natural for many scalability challenges to arise. In this paper, we discuss system management and control, including machine booting, software installation, user account management, system monitoring, and job execution. We address the issue of scalability by organizing the system hierarchically. The 65536 compute nodes are organized in 1024 clusters of 64 compute nodes each, called processing sets. Each processing set is under control of a 65th node, called an I/O node. The 1024 processing sets can then be managed to a great extent as a regular Linux cluster, of which there are several successful examples. Regular cluster management is complemented by BlueGene/L specific services, performed by a service node over a separate control network. Our software development and experiments have been conducted so far using an architecturally accurate simulator of BlueGene/L, and we are gearing up to test real prototypes in 2003.

Interactive parallel visualization framework for distributed data

A framework for parallel visualization at Pacific Northwest National Laboratory (PNNL) is being developed that utilizes the IBM Scaleable Graphics Engine (SGE) and IBM SP parallel computers. Parallel visualization resources are discussed, including display technologies, data handling, rendering, and interactivity. Several of these resources have been developed, while others are under development. These framework resources will be utilized by programmers in custom parallel visualization applications.

Scalable visualization using a network-attached video framebuffer

Abstract We present a scalable, commodity-based, parallel rendering system for interactive visualization of large polygonal and volumetric data models. Our system utilizes commodity PCs that have multiple CPUs and high-capacity I/O buses, a fast AGP bus, and a commodity interconnect. Rendering occurs in parallel using the Chromium framework with the resulting images displayed over the network on a remote display. A key component of our system is the Scalable Graphics Engine, a network-attached video framebuffer capable of gathering pixels from up to 16 sources and driving multiple displays. Our experimental results show that recent developments in commodity computers favor parallel architectures designed to use framebuffer readback and pixel transfer over commodity networks versus specialized hardware for acquiring and aggregating pixel data.