TY - GEN
T1 - AIWC
T2 - 5th IEEE/ACM Workshop on the LLVM Compiler Infrastructure in HPC, LLVM-HPC 2018
AU - Johnston, Beau
AU - Milthorpe, Josh
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Measuring performance-critical characteristics of application workloads is important both for developers, who must 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 characteristics are tied to architectural features that are specific to a particular system, they may not generalize well to alternative or future systems. 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 work 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 parallel HPC architecture. The tool, AIWC, is a plugin for the open-source Oclgrind simulator. It supports parallel workloads and is capable of characterizing OpenCL codes currently in use in the supercomputing setting. AIWC simulates an OpenCL device by directly interpreting LLVM instructions, and the resulting metrics may be used for performance prediction and developer feedback to guide device-specific optimizations. An evaluation of the metrics collected over a subset of the Extended OpenDwarfs Benchmark Suite is also presented.
AB - Measuring performance-critical characteristics of application workloads is important both for developers, who must 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 characteristics are tied to architectural features that are specific to a particular system, they may not generalize well to alternative or future systems. 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 work 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 parallel HPC architecture. The tool, AIWC, is a plugin for the open-source Oclgrind simulator. It supports parallel workloads and is capable of characterizing OpenCL codes currently in use in the supercomputing setting. AIWC simulates an OpenCL device by directly interpreting LLVM instructions, and the resulting metrics may be used for performance prediction and developer feedback to guide device-specific optimizations. An evaluation of the metrics collected over a subset of the Extended OpenDwarfs Benchmark Suite is also presented.
KW - analysis
KW - workload characterisation
UR - http://www.scopus.com/inward/record.url?scp=85063100149&partnerID=8YFLogxK
U2 - 10.1109/LLVM-HPC.2018.8639381
DO - 10.1109/LLVM-HPC.2018.8639381
M3 - Conference contribution
T3 - Proceedings of LLVM-HPC 2018: 5th Workshop on the LLVM Compiler Infrastructure in HPC, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis
SP - 81
EP - 91
BT - Proceedings of LLVM-HPC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 November 2018
ER -