About Me
I am a Systems Engineer at Scaled Inference, a company that aims to empower people through intelligent computing. Previously, I was a researcher at Qualcomm Research Silicon Valley. Prior to that, I graduated from Princeton University in 2014 with a Ph.D. degree in Electrical Engineering.
Research
At Qualcomm, I investigated how to utilize the many heterogeneous processors in a mobile system using an asynchronous parallel software library called MARE.
Previously, during my Ph.D. study, I focused on making massively parallel processors such as graphics processing units (GPUs) easier to program and run faster. Toward that goal, I have worked on a few projects:
- To help GPU designers and users make tuning decisions, I apply statistical techniques such as linear regression and decision trees to model GPU hardware and software design spaces. The result is two fully automated statistical tools that efficiently sample designs, accurately capture performance/power trends using these samples, and output easy-to-understand models.
- To transparently improve the memory system performance for GPU users, I study and improve the utility of general-purpose, read-write caches in GPUs. I developed both microarchitectural units and compile-time algorithms to make GPU caches more effective without the need for user intervention.
In general, I'm interested in studying and improving the power and performance of emerging computing platforms such as accelerators, mobile systems, and distributed systems. I want to make these platforms perform well while being easy to use.
Work Experience
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Scaled Inference Inc., Palo Alto, CA, since October, 2015
Systems Engineer
Developing a machine learning online service platform -
Qualcomm Inc., Santa Clara, CA, October, 2014–October, 2015
Senior Engineer
Researched and developed an asynchronous parallel software library -
Google Inc., Mountain View, CA, June–September, 2011
Software Engineering Intern
Studied function-level program code multi-versioning in GCC -
IBM T.J. Watson Research Center, Hawthorne, NY, June–September, 2010
Research Intern
Parallelized and optimized an MPI-based business analytics application
Education
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Princeton University, Department of Electrical Engineering
Ph.D., 2014
M.A., 2011 -
Tsinghua University, Department of Electrical Engineering
B.S., 2008
Publications
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GPU Performance and Power Tuning Using Regression Trees
Wenhao Jia, Elba Garza, Kelly A. Shaw, Margaret Martonosi
Volume 12, Issue 2, Ariticle 13, ACM Transactions on Architecture and Code Optimization (TACO) -
Analysis and Optimization Techniques for Massively Parallel Processors
Wenhao Jia
Ph.D. Thesis, advised by Prof. Margaret Martonosi and Prof. Kelly A. Shaw -
MRPB: Memory Request Prioritization for Massively Parallel Processors
Wenhao Jia, Kelly A. Shaw, Margaret Martonosi
The 20th Int. Symp. on High Performance Computer Architecture (HPCA 2014) -
Starchart: Hardware and Software Optimization Using Recursive Partitioning Regression Trees
Wenhao Jia, Kelly A. Shaw, Margaret Martonosi
The 22nd Int. Conf. on Parallel Architectures and Compilation Techniques (PACT 2013) -
Characterizing and Improving the Use of Demand-Fetched Caches in GPUs
Wenhao Jia, Kelly A. Shaw, Margaret Martonosi
The 26th Int. Conf. on Supercomputing (ICS 2012) -
Stargazer: Automated Regression-Based GPU Design Space Exploration
Wenhao Jia, Kelly A. Shaw, Margaret Martonosi
The 2012 Int. Symp. on Performance Analysis of Systems and Software (ISPASS 2012)
[Best Paper Award Nomination]
Presentations and Posters
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Design Space Analysis for Heterogeneous Systems
Wenhao Jia, Tae Jun Ham, Kelly A. Shaw, Margaret Martonosi
Intel Science &Technology Center for Cloud Computing (ISTC-CC) Annual Retreat 2013 -
Analyzing and Optimizing GPU Communication and Computation
Wenhao Jia, Kelly A. Shaw, Margaret Martonosi
Intel Science & Technology Center for Cloud Computing (ISTC-CC) Annual Retreat 2012 -
A System-Level ISA and Its Applications to Energy-Performance-Reliability Scheduling and Scratchpad Allocation
Yavuz Yetim, Wenhao Jia, Margaret Martonosi, Sharad Malik, Kelly A. Shaw
Gigascale Systems Research Center (GSRC) Annual Symposium 2010 -
System-level ISA (SISA)
Wenhao Jia, Margaret Martonosi, Kelly A. Shaw
Gigascale Systems Research Center (GSRC) Annual Symposium 2009
Software Releases
- Stargazer: A linear regression-based GPU design space exploration tool.
- Starchart: A regression tree-based GPU hardware/software optimization tool.
- HotCRP Beamer Generator: Scripts for generating program committee meeting slides from HotCRP.