Modern large-scale scientific computation problems must execute in a parallel computational environment to achieve acceptable performance. Target parallel environments range from the largest tightly-coupled supercomputers to heterogeneous clusters of workstations. Grid technologies make Internet execution more likely. Hierarchical and heterogeneous systems are increasingly common. Processing and communication capabilities can be nonuniform, non-dedicated, transient or unreliable. Even when targeting homogeneous computing environments, each environment may differ in the number of processors per node, the relative costs of computation, communication, and memory access, and the availability of programming paradigms and software tools. Architecture-aware computation requires knowledge of the computing environment and software performance characteristics, and tools to make use of this knowledge. These challenges may be addressed by compilers, low-level tools, dynamic load balancing or solution procedures, middleware layers, high-level software development techniques, and choice of programming languages and paradigms. Computation and communication may be reordered. Data or computation may be replicated or a load imbalance may be tolerated to avoid costly communication. This paper samples a variety of approaches to architecture-aware parallel computation.
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