Dynamic Optimization for Interactive
Computing Systems
Parallel Computing Laboratory
Sarah Bird
February 23, 2012
Multicore Revolution
• Parallel Computing is becoming ubiquitous
– Only way forward for computing industry (unless you
don’t care if your apps never run faster than in 2008)
– Unfortunately, parallel programming is (still) harder
than sequential programming
Harness the power of parallelism for client applications
Bridging the Gap
Parallel
Applications
Parallel
Hardware
IT industry
Parallel
Software
Users
Krste Asanovic, Ras Bodik, Jim Demmel, Armando Fox,
Tony Keaveny, Kurt Keutzer, John Kubiatowicz, Nelson Morgan,
Dave Patterson, Koushik Sen, John Wawrzynek,
David Wessel, and Kathy Yelick
Pediatric MRI
Typical exam ~ 1 hour
Motion blurs the images
Scanner is a small loud tunnel
Difficult for children to stay still!
Traditional Solution: Anesthesia
Compressed Sensing
reduces each scan to
15 seconds
Takes too long to
reconstruct image
~ Hours
Compressed Sensing for Pediatric MRI
Image reconstruction from
1-2 hours down to < 1 min
PACORA
How do I guarantee interactivity on my multicore device when it’s
running a bunch of apps?
Speech Decoder
Apps don’t miss deadlines
 Turn off unnecessary resources
 Developers don’t need to understand
hardware
Penalty

s = slope
d
Service
Requirement
Runtime
Runtime
OS Resource Allocation Framework
t (w, b) = R 0 + å w i, j
i, j
bi * b j
Flawless user experience
while maximizing battery life!
More Great ParLab Research
Communication-Avoiding Linear Algebra
• Order of magnitude speedups over optimized code
• 8.8x faster than Intel’s MKL
App
Dense Sparse
Multicore
GPU
Music Application Research
• New user interfaces with
pressure-sensitive multitouch gestural interfaces
ParLab SEJITS project:
Selected Embedded Just-in-Time Specialization
 Asp: “Asp is SEJITS in Python” general
specializer framework
 Performance of highly optimized C with the
productivity of Python!
Parallel Computing Laboratory
• User-centric research agenda
• Better user-interface programming
across diversity of devices
• Data capsules for secure data access
• Heterogeneity to improve
performance and reduce energy
• Dynamic client+cloud partitioning to
improve efficiency
Future of Personal Computing
Join us at ParLab for Lunch!
5th Floor Soda Hall
A RealTime, Parallel GUI Service in Tessellation ManyCore OS
Synthesizing a Parallel Web Browser Layout Engine
An Automatic Parallelizing and Vectorizing Compiler for Python Loop-Nests
Enabling Specialization via MapReduce
Accelerating Graph Algorithms by Software Optimization & Hardware Modification
Characterizing Memory Hierarchies of Multicore Processors Using Microbenchmarks
Garbage Collection on GPUs
Debugging SEJITS
Hardware Communication Channels for Quality-of-Service Enforcement
OLOV: OpenCL for OpenCV
Megh: A Cloud Backed File System
Parallelizing Machine Translation Training Pipeline with Hadoop
CDT: An interactive compiler translation debugger for SEJITS specializers
PACORA: Performance-Aware Convex Optimization for Resource Allocation
pOSKI Project Updates
SEJITS in the Cloud
Communication Costs of LU Decomposition Algorithms for Banded Matrices