No abstract

Kimberley enables rapid software provisioning of fixed infrastructure for transient use by a mobile device. It uses virtual machine (VM) technology, but avoids the performance challenges of running VMs on resource-poor mobile devices. VM execution only occurs in the infrastructure and never on the mobile device; however, the device may transport and interpret parts of VM state. Kimberley decomposes VM state into a widely-available base VM and a much smaller private VM overlay. The base is downloaded in advance; the overlay is delivered on demand from the mobile device or under its control from a public web site. We have built a prototype of Kimberley, and our experiments confirm the feasibility of this approach.

Abstract. The complexity of modern computing platforms has made it extremely difficult to write numerical code that achieves the best possible performance. Straightforward implementations based on algorithms that minimize the operations count often fall short in performance by at least one order of magnitude. This tutorial introduces the reader to a set of general techniques to improve the performance of numerical code, focusing on optimizations for the computer's memory hierarchy. Further, program generators are discussed as a way to reduce the implementation and optimization effort. Two running examples are used to demonstrate these techniques: matrix-matrix multiplication and the discrete Fourier transform.

The Cell BE is a multicore processor with eight vector accelerators (called SPEs) that implement explicit cache management through direct memory access engines. While the Cell has an impressive floating point peak performance, programming and optimizing for it is difficult as it requires explicit memory management, multithreading, streaming, and vectorization. We address this problem for the discrete Fourier transform (DFT) by extending Spiral, a program generation system, to automatically generate highly optimized implementations for the Cell. The extensions include multi-SPE parallelization and explicit memory streaming, both performed at a high abstraction level using rewriting systems operating on Spiral's internal domain-specific language. Further, we support latency and throughput optimizations, single and double precision, and different data formats. The performance of Spiral's computer generated code is comparable with and sometimes better than existing DFT implementations, where available.

Abstract. This paper presents a program generator for fast software Viterbi decoders for arbitrary convolutional codes. The input to the generator is a specification of the code and a single-instruction multiple-data (SIMD) vector length. The output is an optimized C implementation of the decoder that uses explicit Intel SSE vector instructions. At the heart of the generator is a small domain-specific language called VL to express the structure of the forward pass. Vectorization is done by rewriting VL expressions, which a compiler then translates into actual code in addition to performing further optimizations specific to the vector instruction set. Benchmarks show that the generated decoders match the performance of available expert hand-tuned implementations, while spanning the entire space of convolutional codes. An online interface to the generator is provided at www.spiral.net.