Chen Tianqi

Henriksen Troels

Lee Seyong

Leissa Roland

McDonell Trevor L.

Steuwer Michel

Tartara Michele

English

High-level functional programming abstractions have started to show promising results for HPC (High-Performance Computing). Approaches such as Lift, Futhark or Delite have shown that it is possible to have both, high-level abstractions and performance, even for HPC workloads such as stencils. In addition, these high-level functional abstractions can also be used to represent programs and their optimized variants, within the compiler itself. However, such high-level approaches rely heavily on the compiler to optimize programs which is notoriously hard when targeting GPUs.Compilers either use hand-crafted heuristics to direct the optimizations or iterative compilation to search the optimization space. The first approach has fast compile times, however, it is not performance-portable across different devices and requires a lot of human effort to build the heuristics. Iterative compilation, on the other hand, has the ability to search the optimization space automatically and adapts to different devices. However, this process is often very time-consuming as thousands of variants have to be evaluated. Performance models based on statistical techniques have been proposed to speed up the optimization space exploration. However, they rely on low-level hardware features, in the form of performance counters or low-level static code features.Using the Lift framework, this paper demonstrates how low-level, GPU-specific features are extractable directly from a high-level functional representation. The Lift IR (Intermediate Representation) is in fact a very suitable choice since all optimization choices are exposed at the IR level. This paper shows how to extract low-level features such as number of unique cache lines accessed per warp, which is crucial for building accurate GPU performance models. Using this approach, we are able to speed up the exploration of the space by a factor 2000x on an AMD GPU and 450x on Nvidia on average across many stencil applications

Remmelg, Toomas

Hagedorn, Bastian

Li, Lu

Steuwer, Michel

Gorlatch, Sergei

Dubach, Christophe

Edinburgh Research Explorer

High-Level Hardware Feature Extractionfor GPU Performance Prediction of Stencils

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High-level hardware feature extraction for GPU performance prediction of stencils

https://www.pure.ed.ac.uk/ws/files/133854286/High_Level_Hardware_REMMELG_DOA20012020_AFV.pdf

High-Level Hardware Feature Extractionfor GPU Performance Prediction of Stencils

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