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Exploiting Parallelism on GPUs

Exploiting Parallelism on GPUs. Matt Mukerjee David Naylor. Parallelism on GPUs. $100 NVIDIA video card  192 cores (Build Blacklight for ~$2000 ???) Incredibly low power Ubiquitous Question: Use for general computation? General Purpose GPU (GPGPU). ?. =. GPU Hardware.

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Exploiting Parallelism on GPUs

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  1. Exploiting Parallelism on GPUs Matt Mukerjee David Naylor

  2. Parallelism on GPUs • $100 NVIDIA video card  192 cores • (Build Blacklight for ~$2000 ???) • Incredibly low power • Ubiquitous • Question: Use for general computation? • General Purpose GPU (GPGPU) ? =

  3. GPU Hardware • Very specific constraints • Designed to be SIMD (e.g. shaders) • Zero-overhead thread scheduling • Little caching (compared to CPUs) • Constantly stalled on memory access • MASSIVE # of threads / core • Much finer-grained threads (“kernels”)

  4. CUDA Architecture

  5. Thread Blocks • GPUs are SIMD • How does multithreading work? • Threads that branch are halted, then run • Single Instruction Multiple….?

  6. CUDA is an SIMT architecture • Single Instruction Multiple Thread • Threads in a block execute the same instruction Multi-threaded Instruction Unit

  7. Observation Fitting the data structures needed by the threads in one multiprocessor requires application-specific tuning.

  8. Example: MapReduce on CUDA Too big for cache on one SM!

  9. Problem Only one code branchwithin a block executes at a time

  10. Enhancing SIMT

  11. Problem If two multiprocessors share a cache line, there are more memory accesses than necessary.

  12. Data Reordering

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