GPU Performance Prediction
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Javier DelgadoGabriel Gazolla
Constantinos MenelaouLixi Wang
Mark Joselli
Outline
Motivation Role in Energy Efficiency Performance Modeling GPU programming for Weather Modeling GPU Programming for BLAST Model Testing Conclusion
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Benefits
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
GPU Performance Improvement Over Time
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Source: nVidia.com
Sample Speedups
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Source: nVidia.com
Outline
Motivation Role in Energy Efficiency Performance Modeling GPU programming for Weather Modeling GPU Programming for BLAST Model Testing Conclusion
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Role in Energy Efficiency
Idle GPU = wasted energy Maximally-loaded GPU = a lot of power
consumption For example
Nvidia 8800 GTX consumes 137W @ max load Intel Xeon LS5400 consumes 50W @ max load
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Source: http://mark.zoomcities.com/images/gfx/GFXpowerchartby3d.png (which is derived from data from http://www.xbitlabs.com)
Power Consumption
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
http://www.xbitlabs.com/articles/video/display/gf8800gts320MB-roundup_8.html#sect0
http://www.xbitlabs.com/articles/video/display/xfx-gf-gtx285-gtx295_16.html
GPU Role in Energy Efficiency
But...
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Source: John Michalakes and Manish Vachharajani
• And ...
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Outline
Motivation Role in Energy Efficiency Hurricane Mitigation Overview Performance Modeling GPU Programming for BLAST Model Testing Conclusion
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Motivation
Hurricanes cost coastal regions financial and personal damage
Damage can be mitigated, but
Impact area prediction is inaccurate
Simulation using commodity computers is not precise
Alarming Statistics
40% of (small-medium sized) companies shut down within 36 months,
if forced closed for 3 or more days after a hurricane
Local communities lose jobs and hundreds of millions of dollars to their
economy
If 5% of businesses in South Florida recover one week earlier,
then we can prevent $219,300,000 in non-property economic
losses
Hurricane Andrew, Florida 1992 Katrina, New Orleans 2005 Ike, Cuba 2008
Outline
Motivation Role in Energy Efficiency Hurricane Mitigation Overview Performance Modeling GPU Programming for BLAST Model Testing Conclusion
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Motivation for application profiling and performance
prediction Optimal usage of grid resources through “smarter”
meta-scheduling Many users overestimate job requirements Reduced idle time for compute resources Save utility and energy costs Optimal resource selection for most expedient job
return time
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Process
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Typical Results on Large Clusters
Input: Marenostrum– 8, 16, and 32 nodes– 1 process per node
Output: Marenostrum– 8, 16, 32, 64, 96,
and 128 nodes
0 20 40 60 80 100 120 140
0
200
400
600
800
1000
1200
Actual Execution Time (s)Predicted Execution Time (s)
Number of Nodes
Exe
cutio
n T
ime
(s)
Future Modeling Plans
Model execution time with different GPU configurations
Current GPU project objective: learn how to model GPU performance by porting WRF kernels to CUDA Test with different cards Test with different processor configurations Test with different number of nodes
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Overview of GPU Benchmarking Project
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Understand Source code of existing CUDA-ported code
Understand old source code (Fortran)
Learn CUDA
Port another module
Benchmark
Learn WRF
Learn WRF
Learn CUDA
Learn Fortran
Status
Code has been compiled and executed Regions of similarity are being identified
– Fortran Program: 1729 lines
– CUDA (C) Program: 1329 lines (incl init) Currently figuring out necessary code logic of
existing ported kernel Preliminary documentation/report of findings
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Outline
Motivation Role in Energy Efficiency Hurricane Mitigation Overview Performance Modeling GPU Programming for BLAST Model Testing Conclusion
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Purpose
BLAST used extensively for sequence analysis Provides a different kind of application for
testing GPU performance improvements Further improve our GPU programming and
performance modeling knowledge
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Status
Literature review concerning other sequence analysis work with GPU
Learning how BLAST works
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Long-running, Fault-tolerant Weather Prediction
Slight inaccuracies in initial conditions of domain can cause significant inaccuracies later
Third component of this project: account for this using perturbation analysis
The effects of perturbation on runtime must also be modeled
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.
Conclusion
GPU’s promise much faster job execution for different applications
In order to maximize resource utilization, application execution time should be predictable Especially for time-critical applications that take long
to execute
GreenLight Education & Outreach Summer WorkshopUCSD. La Jolla, California. July 1 – 2, 2009.