Nested data-parallelism on the gpu

  title={Nested data-parallelism on the gpu},
  author={Lars Bergstrom and John H. Reppy},
Graphics processing units (GPUs) provide both memory bandwidth and arithmetic performance far greater than that available on CPUs but, because of their Single-Instruction-Multiple-Data (SIMD) architecture, they are hard to program. Most of the programs ported to GPUs thus far use traditional data-level parallelism, performing only operations that operate uniformly over vectors. NESL is a first-order functional language that was designed to allow programmers to write irregular-parallel programs… CONTINUE READING
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