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Introduction
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Michaels background
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Why Im here
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Outline
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Recap
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Right abstraction
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Task vs data parallelism
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Latency and bandwidth
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DMV example
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Flynns Taxonomy
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CPU vs GPU
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Multicore CPU
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Architectures
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Memory bound problem
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Memory optimization
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Pad properly
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Data layout
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Power of computing
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What happened
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What happened in 2011
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CPU vs GPU performance
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GPU explosion
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Hardware
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GPU programming
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Parallelization and concurrency
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Heterogeneity
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Consumer AI
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GPU languages
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C executives
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How GPUs work
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How CPUs work
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How GPU work
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Memory regions
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Multiple work items
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Wavefronts
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Lockstep
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Kernel barriers
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Summary
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Code
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SpinD
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ND Range
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Sickle
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Chronos
Description:
Explore GPU programming with modern C++ in this comprehensive ACCU 2019 conference talk. Delve into the fundamentals of parallelism, learn to recognize when to use it, and discover common parallel patterns like reduce, map, and scan. Gain insights into utilizing the C++ standard threading library and extending parallelism to heterogeneous devices using the SYCL programming model for GPU implementation. Understand the differences between CPU and GPU architectures, memory optimization techniques, and the evolution of GPU performance. Cover topics such as task vs. data parallelism, latency and bandwidth, Flynn's Taxonomy, memory-bound problems, and the power of computing. Learn about GPU programming languages, how GPUs work, memory regions, multiple work items, wavefronts, and kernel barriers. Presented by Michael Wong, Vice President of Research and Development at Codeplay Software and Chair of the C++ Heterogeneous Programming language SYCL, this talk offers valuable insights for developers looking to harness the power of parallel programming in modern C++. Read more

GPU Programming with Modern C++

ACCU Conference
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