Static single assignment (SSA) form is a program representation becoming increasingly popular for compiler-based code optimization. In this paper, we address three problems that have arisen in our use of SSA form. Two are variations to the SSA construction algorithms presented by Cytron et al

successful prefetching schemes. Based on this guideline, we design three prefetching schemes, we automate the most widely applicable scheme (greedy prefetching) in an optimizing research compiler, and we evaluate the performance of all three schemes on a modern superscalar processor similar to the MIPS R

-world workloads. In this thesis, we study compiler-based pre-execution to construct prefetching helper threads using a source-level compiler. We first introduce various compiler algorithms to optimize the helper threads; program slicing removes noncritical code unnecessary to compute the delinquent loads

that unifies the various transforms as unimodular matrix transformations. The algorithm has been implemented in the SUIF (Stanford University Intermediate Format) compiler, and is successful in optimizing codes such as matrix multiplication, successive over-relaxation (SOR), LU decomposition without pivoting

is beneficial for peer-to-peer content distribution, since it eliminates the need for content reconciliation, and is highly resilient to peer failures. In this paper, we present our recent experiences with a highly optimized and high-performance C++ implementation of randomized network coding at the application

in applications that are inefficient and hard to maintain and extend. Increasingly, software system professionals are discovering just how difficult it is to work with these inherited, "non-optimal" applications. For several years, expert-level object programmers have employed a growing collection

stop criterion derived from cross entropy, which results in a minimal number of iterations. Optimal and suboptimal decoders with reduced complexity are presented. Simulation results show that very simple component codes are sufficient, block codes are appropriate for high rates and convolutional codes

of the space–time block code and gives a maximum-likelihood decoding algorithm which is based only on linear processing at the receiver. Space–time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple

We design low-density parity-check (LDPC) codes that perform at rates extremely close to the Shannon capacity. The codes are built from highly irregular bipartite graphs with carefully chosen degree patterns on both sides. Our theoretical analysis of the codes is based on [1]. Assuming

. The decoding of both codes can be tackled with a practical sum-product algorithm. We prove that these codes are "very good," in that sequences of codes exist which, when optimally decoded, achieve information rates up to the Shannon limit. This result holds not only for the binary-symmetric channel