Lucas Salvador will present his MSE Talk ":Demystifying the GPU Stall Cycle: Evaluating the Diagnosis of GPU Bottlenecks" on Wednesday 4/28/2021 at 2PM via Zoom.
Lucas Salvador will present his MSE Talk " Demystifying the GPU Stall Cycle: Evaluating the Diagnosis of GPU Bottlenecks" on Wednesday 4/28/2021 at 2PM via Zoom. Zoom Link: [ https://princeton.zoom.us/j/96305827497 | https://princeton.zoom.us/j/96305827497 ] Committee: David August (Adviser), Mohammad Shahrad (Reader) All are welcome to attend. Abstract: Graphics Processing Units (GPUs) have become ubiquitous in all modern intensive computing environments, having transitioned from their early use in Graphics to a plethora of other general compute applications. These tasks are all equally demanding, yet their distinct access patterns generate specific strains in the GPU hardware that evidence different bottlenecks. In order to extract the maximum performance possible from a GPU, application designers and architecture engineers typically have to work back from the symptoms, relying in a series of "rules of thumb" and abstract tools to distill insights that may or may not be related to the actual application bottlenecks. This has led to a series of techniques that have attempted, through the use of detailed simulation, to isolate the cause of bottlenecks in GPU devices. These approaches rely on a hierarchy of stall causes that attributes different importance to specific bottlenecks in the GPU pipeline. In this work, we evaluate the validity of these hierarchical approaches by comparing their insights with the results we could expect from a "perfect" GPU, that is, a simulation (run on GPGPU-Sim) where each specific bottleneck is fully removed, one at a time. By comparing the results across a series of benchmarks, we expose the limitations of these approaches, indicating how the flawed insights may result in misguided optimization. We then further discuss the conceptual pitfalls that seem to be responsible for these discrepancies, and use these to outline a series of desired properties we would expect from a successful diagnosis technique.
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