Rinnegan: Efficient resource use in heterogeneous architectures

Current processors provide a variety of different processing units to improve performance and power efficiency. For example, ARM's big.LITTLE, AMD's APUs, and Oracle's M7 provide heterogeneous processors, on-die GPUs, and on-die accelerators. However, the performance experienced by pr...

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Vydané v:2016 International Conference on Parallel Architecture and Compilation Techniques (PACT) s. 373 - 386
Hlavní autori: Panneerselvam, Sankaralingam, Swift, Michael
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Jazyk:English
Vydavateľské údaje: ACM 01.09.2016
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Central Processing Unit
Kernel
Monitoring
Processor scheduling
Program processors
Resource management
Runtime
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Abstract Current processors provide a variety of different processing units to improve performance and power efficiency. For example, ARM's big.LITTLE, AMD's APUs, and Oracle's M7 provide heterogeneous processors, on-die GPUs, and on-die accelerators. However, the performance experienced by programs using these processing units can vary widely due to contention from multiprogramming, thermal constraints and other issues. In these systems, the decision of where to execute a task must consider not only execution time of the task, but also current system conditions. We built Rinnegan, a Linux kernel extension and runtime library, to perform scheduling and handle task placement in heterogeneous systems. The Rinnegan kernel extension monitors and reports the utilization of all processing units to applications, which then makes placement decisions at user level. The Rinnegan runtime provides a performance model to predict the speedup and overhead of offloading a task. With this model and the current utilization of processing units, the runtime can select the task placement that best achieves an application's performance goals, such as low latency, high throughput, or real-time deadlines. When integrated with StarPU, a runtime system for heterogeneous architectures, Rinnegan improves StarPU by performing 1.5- 2× better than its native scheduling policies in a shared heterogeneous environment.
AbstractList Current processors provide a variety of different processing units to improve performance and power efficiency. For example, ARM's big.LITTLE, AMD's APUs, and Oracle's M7 provide heterogeneous processors, on-die GPUs, and on-die accelerators. However, the performance experienced by programs using these processing units can vary widely due to contention from multiprogramming, thermal constraints and other issues. In these systems, the decision of where to execute a task must consider not only execution time of the task, but also current system conditions. We built Rinnegan, a Linux kernel extension and runtime library, to perform scheduling and handle task placement in heterogeneous systems. The Rinnegan kernel extension monitors and reports the utilization of all processing units to applications, which then makes placement decisions at user level. The Rinnegan runtime provides a performance model to predict the speedup and overhead of offloading a task. With this model and the current utilization of processing units, the runtime can select the task placement that best achieves an application's performance goals, such as low latency, high throughput, or real-time deadlines. When integrated with StarPU, a runtime system for heterogeneous architectures, Rinnegan improves StarPU by performing 1.5- 2× better than its native scheduling policies in a shared heterogeneous environment.
Author Swift, Michael
Panneerselvam, Sankaralingam
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  givenname: Michael
  surname: Swift
  fullname: Swift, Michael
  email: swift@cs.wisc.edu
  organization: Univ. of Wisconsin-Madison, Madison, WI, USA
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Snippet Current processors provide a variety of different processing units to improve performance and power efficiency. For example, ARM's big.LITTLE, AMD's APUs, and...
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StartPage 373
SubjectTerms Central Processing Unit
Kernel
Monitoring
Processor scheduling
Program processors
Resource management
Runtime
Title Rinnegan: Efficient resource use in heterogeneous architectures
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