Heinz Baier

IBM BladeCenter QS22: design, performance, and utilization in hybrid computing systems

The IBM BladeCenter® QS22 is a blade server that is based on two IBM PowerXCell™ 8i processors. This server is the successor of the QS21 server, which delivered excellent multicore Cell Broadband Engine® (Cell/B.E.) performance for workstation, server, and supercomputer customers. The IBM PowerXCell 8i processor represents a second generation of the Cell Broadband Engine Architecture. It significantly improves floating-point performance and allows for larger memory configurations. In this paper, we provide an overview of the architecture and design of this newer QS22 system and explain some of the design decisions. We discuss hardware and application performance, power efficiency, manageability, and configuration options, and how these factors affect hybrid system configurations. Finally, we briefly describe two hybrid system configurations that utilize the QS22 system. One configuration involves the Roadrunner system at Los Alamos National Laboratory, the first one-petaflops supercomputer in the world. The second configuration involves a hybrid system with a PCI Express® switch used for coupling.

Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications

This paper describes the hardware and software ecosystem encompassing the brain-inspired TrueNorth processor – a 70mW reconfigurable silicon chip with 1 million neurons, 256 million synapses, and 4096 parallel and distributed neural cores. For systems, we present a scale-out system loosely coupling 16 single-chip boards and a scale-up system tightly integrating 16 chips in a 4 × 4 configuration by exploiting TrueNorths native tiling. For software, we present an end-to-end ecosystem consisting of a simulator, a programming language, an integrated programming environment, a library of algorithms and applications, firmware, tools for deep learning, a teaching curriculum, and cloud enablement. For the scale-up systems we summarize our approach to physical placement of neural network, to reduce intra- and inter-chip network traffic. The ecosystem is in use at over 30 universities and government/corporate labs. Our platform is a substrate for a spectrum of applications from mobile and embedded computing to cloud and supercomputers.