Languages and Compilers for Parallel Computing

CUDA-Lite: Reducing GPU Programming Complexity.- MCUDA: An Efficient Implementation of CUDA Kernels for Multi-core CPUs.- Automatic Pre-Fetch and Modulo Scheduling Transformations for the Cell BE Architecture.- Efficient Set Sharing Using ZBDDs.- Register Bank Assignment for Spatially Partitioned Processors.- Smashing: Folding Space to Tile through Time.- Identification of Heap-Carried Data Dependence Via Explicit Store Heap Models.- On the Scalability of an Automatically Parallelized Irregular Application.- Statistically Analyzing Execution Variance for Soft Real-Time Applications.- Minimum Lock Assignment: A Method for Exploiting Concurrency among Critical Sections.- Set-Congruence Dynamic Analysis for Thread-Level Speculation (TLS).- Thread Safety through Partitions and Effect Agreements.- P-Ray: A Software Suite for Multi-core Architecture Characterization.- Scalable Implementation of Efficient Locality Approximation.- P-OPT: Program-Directed Optimal Cache Management.- Compiler-Driven Dependence Profiling to Guide Program Parallelization.- gluepy: A Simple Distributed Python Programming Framework for Complex Grid Environments.- A Fully Parallel LISP2 Compactor with Preservation of the Sliding Properties.- A Case Study in Tightly Coupled Multi-paradigm Parallel Programming.- ASYNC Loop Constructs for Relaxed Synchronization.- Design for Interoperability in stapl: pMatrices and Linear Algebra Algorithms.- Implementation of Sensitivity Analysis for Automatic Parallelization.- Just-In-Time Locality and Percolation for Optimizing Irregular Applications on a Manycore Architecture.- Exploring the Optimization Space of Dense Linear Algebra Kernels.