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. It supports parallel workloads and is capable of characterizing OpenCL codes currently in use in the supercomputing setting. The tool, AIWC, is a plugin for the open-source Oclgrind simulator. 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. An architecture-independent method en- sures an accurate characterization of inherent program behaviour, without bias due to architecture-dependent features that vary widely between different types of accelerators. 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. 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. The improvements suggested to AIWC will help HPC developers better understand memory access patterns of complex codes and guide optimization of codes for arbitrary hardware targets. The new metric can be used to distinguish between the OpenDwarfs benchmarks based on the memory access patterns affecting their performance on various architectures. The differences in the observed parallel spatial locality metric across implementations of matrix multiply reflect the optimizations performed. We implement the parallel spatial locality metric in the AIWC framework, and analyse gathered results on matrix multiply and the Extended OpenDwarfs OpenCL benchmarks. We propose a new metric of parallel spatial locality - the closeness of memory accesses simultaneously issued by OpenCL work-items (threads). However, AIWC metrics are not always easily interpreted and do not reflect some important memory access patterns affecting efficiency across architectures.
#CONFERENCE OPENCL BENCHMARK SIMULATOR#
The Architecture-Independent Workload Characterization (AIWC) tool is a plugin for the Oclgrind OpenCL simulator that gathers metrics of OpenCL programs that can be used to understand and predict program performance on an arbitrary given hardware architecture. High-performance computing developers are faced with the challenge of optimizing the performance of OpenCL workloads on diverse architectures.
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