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CPUs and GPUs: can we get the best of both worlds?
Conferenciante(s) : Sylvain Collange (Inria Rennes – Bretagne Atlantique)GPUs are now established parallel accelerators for high-performance
computing applications and machine learning. Part of their success is
based on their so-called SIMT execution model. SIMT binds together
threads of parallel applications so they perform the same instruction at
the same time, in order to execute their instructions on
energy-efficient SIMD units. Unfortunately, current GPU architectures
lack the flexibility to work with standard instruction sets like x86 or
ARM. Their implementation of SIMT requires special instruction sets with
control-flow reconvergence annotations, and they seldom support complex
control flow like exceptions, context switches and thread migration.
In this talk, I will present how we can generalize the SIMT execution
model of GPUs to general-purpose processors and multi-thread
applications, and then use it to design new CPU-GPU hybrid cores. These
hybrid cores will form the building blocks of heterogeneous
architectures, that will combine CPU-like cores and GPU-like cores which
all share the same instruction set and programming model. Beside
improving programmability, generalized SIMT enables key improvements
that were not possible in the traditional SIMD model, such as
simultaneous execution of divergent paths or guarantees of fairness. It
opens the way for a whole spectrum of new architectures, hybrids of
latency-oriented superscalar processors and throughput-oriented SIMT GPUs.
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