PhD Defense by Naila Farooqui

Event Details
  • Date/Time:
    • Monday October 19, 2015
      12:00 pm - 2:00 pm
  • Location: KACB 3100
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Summary Sentence: Runtime Specialization for Heterogeneous CPU-GPU Resources

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Title:  Runtime Specialization for Heterogeneous CPU-GPU Resources

 

Naila Farooqui

School of Computer Science

College of Computing
Georgia Institute of Technology

Date: October 19, 2015 (Monday)
Time: 12:00 PM - 2:00 PM (ET)
Location: KACB 3100

Committee:

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Dr. Karsten Schwan (Advisor, School of Computer Science, Georgia Tech)

Dr. Sudhakar Yalamanchili (School of Electrical and Computer Engineering, Georgia Tech)

Dr. Ada Gavrilovska (School of Computer Science, Georgia Tech)

Dr. Richard Vuduc (School of Computational Science and Engineering, Georgia Tech)

Dr. Vanish Talwar (Research Scientist, PernixData)

Dr. Rajkishore Barik (Research Scientist, Intel Labs)

 

Abstract:

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Heterogeneous parallel architectures like those comprised of CPUs and GPUs are a

tantalizing compute fabric for performance-hungry developers. While these platforms

enable order-of-magnitude performance increases for many data-parallel application

domains, there remain several open challenges: (i) the distinct execution models

inherent in the heterogeneous devices present on such platforms drives the need to

dynamically match workload characteristics to the underlying resources, (ii) the complex

architecture and programming models of such systems require substantial application
 knowledge and effort-intensive program tuning to achieve high performance, and (iii) 
as such platforms become prevalent, there is a need to extend their utility from running 
known regular data-parallel applications to the broader set of input-dependent, irregular 
applications common in enterprise settings. 
 
The key contribution of our research is to enable runtime specialization on such hybrid

CPU-GPU platforms by matching application characteristics to the underlying heterogeneous
resources for both regular and irregular workloads. Our approach enables profile-driven

resource management and optimizations for such platforms, providing high application

performance and system throughput. Towards this end,  this research will: (a) enable dynamic

instrumentation for GPU-based parallel architectures, specifically targeting the complex

Single-Instruction Multiple-Data (SIMD) execution model, to gain real-time introspection into

application behavior; (b) leverage such dynamic performance data to support novel online

resource management methods that improve application performance and system throughput,

particularly for irregular, input-dependent applications; (c) automate some of the programmer

effort required to exercise specialized architectural features of such platforms via

instrumentation-driven dynamic code optimizations; and (d) propose a specialized, affinity-aware

work-stealing scheduler for integrated CPU-GPU processors that efficiently distributes work at

runtime across all CPU and GPU cores for improved load balance, taking into account
 both application characteristics and architectural differences of the underlying devices.


 resources for both regular and irregular workloads. Our approach enables profile-driven

resource management and optimizations for such platforms, providing high application

performance and system throughput. Towards this end,  this research will: (a) enable dynamic

instrumentation for GPU-based parallel architectures, specifically targeting the complex

Single-Instruction Multiple-Data (SIMD) execution model, to gain real-time introspection into

application behavior; (b) leverage such dynamic performance data to support novel online

resource management methods that improve application performance and system throughput,

particularly for irregular, input-dependent applications; (c) automate some of the programmer

effort required to exercise specialized architectural features of such platforms via

instrumentation-driven dynamic code optimizations; and (d) propose a specialized, affinity-aware

work-stealing scheduler for integrated CPU-GPU processors that efficiently distributes work at

runtime across all CPU and GPU cores for improved load balance, taking into account
both application characteristics and architectural differences of the underlying devices.

 

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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Oct 6, 2015 - 5:21am
  • Last Updated: Oct 7, 2016 - 10:14pm