Analyzing the Energy-Time Trade-OFF in high performance computing applications

Presentation done by Kevin Gee
based on Freeh, Lowenthal, Pan, Kappiah, Springer paper 
published in IEEE Transactions on parallel and distributed systems, 
Vol. 18, No. 6, June 2007 

Purpose


  • Demonstrate results of energy-time trade-off of a wide range of applications - serial and parallel - on a power scalable cluster
  • Introduce metrics to predict memory and communication bottlenecks






2

Content


  • Introduction
  • Experimental methodology
  • Single node results
  • Multi node results
  • Metrics
  • Conclusion





3

Introduction: facts



  • HPC  (High Performance Computing) tends to push performance at all cost
  • Last 10% increase in performance requires disproportionally large amounts of resources
  • The Earth Simulator consumes 7 MW of power





4

INTRODUCTION: Solution



  • Low-power high-performance clusters to save energy
  • Green Destiny consumes about 3 times less energy than Accelerated Strategic Computing Initiative (ASCI) Q machine
  • ASCI_Q machine is about 15 times faster per node





5

INTRODUCTION: SOLUTION


  • Using high-performance commodity microprocessor with frequency and voltage scaling
  • Increase in frequency results in:
    • a smaller increase in application performance
    • increase in CPU stalls






6

Introduction: prediction



It is possible to reduce power consumption without a significant increase in execution time








7

Methodology: Programs


  • Single node benchmark sets
    • Nasa Advanced Supercomputing (NAS)
    • System Performance Evaluation Cooperative (SPEC) integer
    • SPEC float
  • Multiple node benchmark sets
    • NAS MPI
    • ASCI



8

METHODOLOGY: Cluster


10 nodes, connected by 100 Mbps network,
 each has:

  • AMD Athlon 64 CPU
  • 1 Gb main memory
  • 128 Kb L1 cache
  • 512 Kb L2 cache




9

METHODOLOGY: Gears











Idle and active power for each AMD-64 node


10

Single node results


Overall results







                      NAS                                        SPEC INT                                   SPEC FP



11

SINGLE NODE RESULTS


Best energy-time trade-off in each set with different programs





12

SINGLE NODE RESULTS


Worst energy-time trade-off using different programs





13

Multiple node results


Bottlenecks:

  • Memory
  • Communication







14

Multiple node results


Possible layouts of the graph :

  • Poor speedup - more energy, less time
  • Perfect or superlinear speedup
  • Speedup is good and there are significant memory operations





15

MULTIPLE NODE RESULTS


Poor speedup (more energy, less time):


16

MULTIPLE NODE RESULTS


Perfect speedup (no energy-time trade-off):



17

MULTIPLE NODE RESULTS


Good speedup:



18

Metrics: CPU criticality



Tmax - execution time in maximum frequency
T - execution time in current frequency
fmax - maximum frequency
f - current frequency

if (beta == 0) => application is independent from CPU 
(T == Tmax)
19

METrics: MPO


  • MPO - Misses Per Operation
  • Measures memory pressure
  • MPO = The number of operations retired and the L2 cache misses( which are memory accesses)
  • Constant while changing frequency
  • If MPO decreases - application becomes more CPU bound





20

Metrics: slack


  • Predicts communication bottleneck
  • Slack - ratio of the total time a node is blocked in MPI calls to the total execution time
  • Greater slack suggests reducing gear
  • Collected on the fly






21

Conclusion


  • In the best case on one node, reducing the CPU speed makes it possible to use 20 percent less energy while increasing time by only 3 percent.

  • On the other hand, a program that is largely CPU bound should be run at the fastest gear possible.

  • MPO and slack can be used to to choose appropriate gear.



22

Your Questions







23
Made with Slides.com