Languages and Compilers for Parallel Computing

Fine-grain scheduling under resource constraints.- Mutation scheduling: A unified approach to compiling for fine-grain parallelism.- Compiler techniques for fine-grain execution on workstation clusters using PAPERS.- Solving alignment using elementary linear algebra.- Detecting and using affinity in an automatic data distribution tool.- Array distribution in data-parallel programs.- Communication-free parallelization via affine transformations.- Finding legal reordering transformations using mappings.- A new algorithm for global optimization for parallelism and locality.- Polaris: Improving the effectiveness of parallelizing compilers.- A formal approach to the compilation of data-parallel languages.- The data partitioning graph: Extending data and control dependencies for data partitioning.- Detecting value-based scalar dependence.- Minimal data dependence abstractions for loop transformations.- Differences in algorithmic parallelism in control flow and call multigraphs.- Flow-insensitive interprocedural alias analysis in the presence of pointers.- Incremental generation of index sets for array statement execution on distributed-memory machines.- A unified data-flow framework for optimizing communication.- Interprocedural communication optimizations for distributed memory compilation.- Analysis of event synchronization in parallel programs.- Computing communication sets for control parallel programs.- Optimizing parallel SPMD programs.- An overview of the Opus language and runtime system.- SIMPLE performance results in ZPL.- Cid: A parallel, "shared-memory" C for distributed-memory machines.- EQ: Overview of a new language approach for prototyping scientific computation.- Reshaping access patterns for generating sparse codes.- Evaluating two loop transformations for reducing multiple-writer false sharing.- Parallelizing tree algorithms: Overhead vs. parallelism.- Autoscheduling in a distributed shared-memory environment.- Optimizing array distributions in