Engineering Commons^™

Vector Coprocessor Sharing Techniques For Multicores: Performance And Energy Gains, Spiridon Florin Beldianu

Dissertations

Vector Processors (VPs) created the breakthroughs needed for the emergence of computational science many years ago. All commercial computing architectures on the market today contain some form of vector or SIMD processing.

Many high-performance and embedded applications, often dealing with streams of data, cannot efficiently utilize dedicated vector processors for various reasons: limited percentage of sustained vector code due to substantial flow control; inherent small parallelism or the frequent involvement of operating system tasks; varying vector length across applications or within a single application; data dependencies within short sequences of instructions, a problem further exacerbated without loop unrolling or other …

Energy-Efficient Multi-Criteria Packet Forwarding In Multi-Hop Wireless Networks, Komlan Egoh

Dissertations

Reliable multi-hop packet forwarding is an important requirement for the implementation of realistic large-scale wireless ad-hoc networks. However, packet forwarding methods based on a single criterion, such as the traditional greedy geographic forwarding, are not sufficient in most realistic wireless settings because perfect-reception-within-rangecannot be assumed. Furthermore, methods where the selection of intermediate relaying nodes is performed at the transmitter-side do not adapt well to rapidly changing network environments. Although a few link-aware geographic forwarding schemes have been reported in the literature, the tradeoffs between multiple decision criteria and their impact on network metrics such as throughput, delay and energy …

High-Performance Matrix Multiplication On Intel And Fgpa Platforms, Gang Li

Theses

Matrix multiplication is at the core of high-performance numerical computation. Software methods of accelerating matrix multiplication fall into two categories. One is based on calculation simplification. The other one is based on increasing the memory access efficiency. Also matrix multiplication can be accelerated using vector processors. In this investigation, various matrix multiplication algorithms and the vector-based hardware acceleration method are analyzed and compared in terms of performance and memory requirements. Results are shown for Intel and Xilinx FPGA platforms. They show that when the CPU is fast, Goto's algorithm runs faster than Strassen's algorithm because the data access speed is …