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Understanding Energy Behaviors of Thread Manage...
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Gustavo Pinto
January 14, 2018
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Understanding Energy Behaviors of Thread Management Constructs
Gustavo Pinto
January 14, 2018
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Transcript
Understanding Energy Behaviors of Thread Management Constructs Gustavo Pinto1 Fernando
Castor1 David Liu2 {ghlp, castor}@cin.ufpe.br1 davidL@binghamton.edu2
2 • First, the proliferation of multicore CPUs • Second,
the prevalence of multi-threaded programs Motivation
3 • First, more cores more power consumed • Second,
little is known about energy behaviors of multi-threaded program on the application and programming language level The Problem
4 1. programming abstractions of thread management on energy efficiency
2. programmer choices of thread management on energy efficiency This Talk for Java multi-threaded programs
5 • Explicit threading (the Thread-style): Using the java.lang.Thread class
• Thread pooling (the Executor-style): Using the java.util.concurrent.Executor framework • Working Stealing (the ForkJoin-style): Using the java.util.concurrent.ForkJoin framework Thread management constructs
6 • Embarrassingly parallel: spectralnorm, sunflow, n-queens • Leaning parallel:
xalan, knucleotide, tomcat • Leaning serial: mandelbrot, largestImage • Embarrassingly serial: h2 Benchmarks
7 • Embarrassingly parallel: spectralnorm, sunflow, n-queens • Leaning parallel:
xalan, knucleotide, tomcat • Leaning serial: mandelbrot, largestImage • Embarrassingly serial: h2 Benchmarks Micro-benchmarks DaCapo benchmarks
8 Experimental Environment A 2×16-core AMD CPUs, running Debian Linux,
64GB of memory, JDK version 1.7.0 11, build 21, “ondemand” governor
9 Experimental Environment A 2×16-core AMD CPUs, running Debian Linux,
64GB of memory, JDK version 1.7.0 11, build 21, “ondemand” governor
10 Experimental Environment A 2×16-core AMD CPUs, running Debian Linux,
64GB of memory, JDK version 1.7.0 11, build 21.
11 Experimental Environment A 2×16-core AMD CPUs, running Debian Linux,
64GB of DDR3 1600 memory, and JDK version 1.7.0 11, build 21.
Energy Consumption When Varying the Number of Threads 12
13 The Λ Curve
14 The Λ Curve
15 The Λ Curve
16 More cores idle CPU frequency at a lower level
The Λ Curve
17 More cores idle CPU frequency at a lower level
More threads used, program completes sooner The greater the ratio between speedup and power, the steeper the \ The Λ Curve
Which programming style should I use? 18 ? ?
Overpopulating Cores with Threads 19
Overpopulating Cores with Threads 20
Faster ≠ Greener 21
Copying vs Sharing 22
Copying vs Sharing 23 Copying
Copying vs Sharing 24 Copying Sharing
Copying vs Sharing 25 Copying Sharing ±15% of energy savings!
26 Copy-Fork
27 After Before Copy-Fork
28 After Before Copy-Fork Copy/Fork/Copy/Fork/…
29 After Before Copy-Fork Copy/Fork/Copy/Fork/…
30 After Before Copy-Fork Copy/Fork/Copy/Fork/… Copy/../Copy/Fork/…/Fork
31 After Before Copy-Fork ±10% of energy savings! Copy/Fork/Copy/Fork/… Copy/../Copy/Fork/…/Fork
Data Size 32
Data Size 33 For most of the cases, energy consumption
is linear to data size!
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Understanding Energy Behaviors of Thread Management Constructs Gustavo Pinto1 Fernando
Castor1 David Liu2 {ghlp, castor}@cin.ufpe.br1 davidL@binghamton.edu2