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Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping
- In Micro-42
, 2009
"... Heterogeneous multiprocessors are growingly important in the multi-core era due to their potential for high performance and energy efficiency. In order for software to fully realize this potential, the step that maps computations to processing elements must be as automated as possible. However, the ..."
Abstract
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Cited by 89 (3 self)
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Heterogeneous multiprocessors are growingly important in the multi-core era due to their potential for high performance and energy efficiency. In order for software to fully realize this potential, the step that maps computations to processing elements must be as automated as possible. However, the state-of-the-art approach is to rely on the programmer to specify this mapping manually and statically. This approach is not only labor intensive but also not adaptable to changes in runtime environments like problem sizes and hardware configurations. In this study, we propose adaptive mapping, a fully automatic technique to map computations to processing elements on heterogeneous multiprocessors. We have implemented it in our experimental heterogeneous programming system called Qilin. Our results demonstrate that, for a set of important computation kernels, automatic adaptive mapping achieves a speedup of 9.3x on average over the best serial implementation by judiciously distributing works over the CPU and GPU, which is 69 % and 33 % faster than using the CPU or GPU alone, respectively. In addition, adaptive mapping is within 94 % of the speedup of the best manual mapping found via exhaustive searching. To the best of our knowledge, Qilin is the first and only system to date that has such capability. 1.
Software and Services Group Intel Corporation
"... Heterogeneous multiprocessors are increasingly important in the multi-core era due to their potential for high performance and energy efficiency. In order for software to fully realize this potential, the step that maps computations to processing elements must be as automated as possible. However, t ..."
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Heterogeneous multiprocessors are increasingly important in the multi-core era due to their potential for high performance and energy efficiency. In order for software to fully realize this potential, the step that maps computations to processing elements must be as automated as possible. However, the state-of-the-art approach is to rely on the programmer to specify this mapping manually and statically. This approach is not only labor intensive but also not adaptable to changes in runtime environments like problem sizes and hardware/software configurations. In this study, we propose adaptive mapping, a fully automatic technique to map computations to processing elements on a CPU+GPU machine. We have implemented it in our experimental heterogeneous programming system called Qilin. Our results show that, by judiciously distributing works over the CPU and GPU, automatic adaptive mapping achieves a 25 % reduction in execution time and a 20 % reduction in energy consumption than static mappings on average for a set of important computation benchmarks. We also demonstrate that our technique is able to adapt to changes in the input problem size and system configuration.
Improving the Productivity of Compiler Code Quality Analysis
, 2009
"... Producing quality code is one of the most important goals of an optimizing compiler. Analyzing code quality is therefore an essential activity in compiler engineering. By motivating new optimizations and diagnosing regressions, it takes a bottleneck position in the process. However, it has been high ..."
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Producing quality code is one of the most important goals of an optimizing compiler. Analyzing code quality is therefore an essential activity in compiler engineering. By motivating new optimizations and diagnosing regressions, it takes a bottleneck position in the process. However, it has been highly empirical, and dependent on architectures and tools. This makes it a difficult and time-consuming task, and its productivity is unpredictable and usually low. This paper proposes two novel approaches for code quality analysis. The first ap-proach focuses on the key scenario in compiler construction, the computation-intensive benchmarks. We observed that the workload upon the processor dominates the execu-tion time of such benchmarks. Therefore, we use the compiler to parse the workload. By doing this, the compiler applies its static analysis power to identify its own code quality issues and potentials. We have implemented a software system for this ap-proach and integrated it with our daily testing infrastructure. It automates the code quality analysis for Spec benchmarks, and provides developers relevant information in
