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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20181221T160728Z
LOCATION:D175
DTSTART;TZID=America/Chicago:20181112T163000
DTEND;TZID=America/Chicago:20181112T170000
UID:submissions.supercomputing.org_SC18_sess176_ws_llvmf110@linklings.com
SUMMARY:AIWC: OpenCL-Based Architecture Independent Workload Characterizat
 ion
DESCRIPTION:Workshop\nProgram Transformation, Programming Systems, Worksho
 p Reg Pass\n\nAIWC: OpenCL-Based Architecture Independent Workload Charact
 erization\n\nJohnston, Milthorpe\n\nMeasuring performance-critical charact
 eristics of application workloads is important both for developers, who mu
 st understand and optimize the performance of codes, as well as designers 
 and integrators of HPC systems, who must ensure that compute architectures
  are suitable for the intended workloads. However, if these workload chara
 cteristics are tied to architectural features that are specific to a parti
 cular system, they may not generalize well to alternative or future system
 s. An architecture-independent method ensures an accurate characterization
  of inherent program behaviour, without bias due to architecture-dependent
  features that vary widely between different types of accelerators. This w
 ork presents the first architecture-independent workload characterization 
 framework for heterogeneous compute platforms, proposing a set of metrics 
 determining the suitability and performance of an application on any paral
 lel HPC architecture. The tool, AIWC, is a plugin for the open-source Oclg
 rind simulator. It supports parallel workloads and is capable of character
 izing OpenCL codes currently in use in the supercomputing setting. AIWC si
 mulates an OpenCL device by directly interpreting LLVM instructions, and t
 he resulting metrics may be used for performance prediction and developer 
 feedback to guide device-specific optimizations. An evaluation of the metr
 ics collected over a subset of the Extended OpenDwarfs Benchmark Suite is 
 also presented.
URL:https://sc18.supercomputing.org/presentation/?id=ws_llvmf110&sess=sess
 176
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