Abstract
Multicore CPUs and GPUs have brought parallel computation within reach of any programmer. How can we put the performance potential of these machines to good use? The contributors of the symposium suggest a number of approaches, among them algorithm engineering, parallel programming languages, compilers that target both SIMD and MIMD architectures, automatic detection and repair of data races, transactional memory, automated performance tuning, and automatic parallelizers. The transition from sequential to parallel computing is now perhaps at the half-way point. Parallel programming will eventually become routine, because advances in hardware, software, and programming tools are simplifying the problems of designing and implementing parallel computations.
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