Janice O. McMahon

Highly scalable near memory processing with migrating threads on the emu system architecture

There is growing evidence that current architectures do not well handle cache-unfriendly applications such as sparse math operations, data analytics, and graph algorithms. This is due, in part, to the irregular memory access patterns demonstrated by these applications, and in how remote memory accesses are handled. This paper introduces a new, highly-scalable PGAS memory-centric system architecture where migrating threads travel to the data they access. Scaling both memory capacities and the number of cores can be largely invisible to the programmer.The first implementation of this architecture, implemented with FPGAs, is discussed in detail. A comparison of key parameters with a variety of todays systems, of differing architectures, indicates the potential advantages. Early projections of performance against several well-documented kernels translate these advantages into comparative numbers. Future implementations of this architecture may expand the performance advantages by the application of current state of the art silicon technology.

Evaluation of Stream Virtual Machine on Raw Processor

Stream processing exploits the properties of stream applications such as parallelism and throughput-oriented nature of the applications. One of the most recent approaches is community-supported Morphware stable interface (MSI) used as a stable abstraction between high-level compilers (HLC) and low-level architecture-specific compilers (LLC). We focus on one part of the MSI, the stream virtual machine (SVM). We implemented a high-level compiler that produces SVM output renderings and SVM implementation. The SVM is implemented with the Raw compiler as the LLC and an accompanying library. We also implemented stream applications such as matrix multiplication, FIR bank, and ground moving target indicator (GMTI) using the implemented compilers. These applications are optimized and the results are analyzed. The results show that the SVM framework is generally suitable for streaming applications on Raw processor.

A Voltage and Resource Synthesis Technique for Energy-Aware Real-time Systems

We consider a resource synthesis technique for realtime systems where dynamic voltage scaling is supported, the energy budget is limited, and the performance of the system depends on how resources and energy are used. We propose a resource synthesis technique that derives both the supply voltages and the resource allocation of the tasks in the system to maximize system performance. The resulting system satisfies real-time schedulability and energy requirements.