"It is never too late to become what you might have been." -- George Elliot

Who am I?

I am the SOAR associate professor (tenured) at the School of Computer Science at University of Sydney, and I direct the Future System Architecture Lab (FSA). I am also a Senior Principal Scientist at Microsoft, leading DeepSpeed4Science initative and other pathfinding projects at DeepSpeed. I hold an Affiliated Professor position with University of Washington . Prior to my appointment at University of Sydney, I worked for U.S. Department of Energy Lab for five years as a senior staff scientist and technical lead. In 2017 and 2022, I was awarded with IEEE HPC early career award and IEEE mid-career award for scalable computing, respectively. I was also awarded with 2022 Alibaba Gloab Faculty Award (AIR), 2022 SOAR Fellowship, 2022/2021 Google Brain Collaboration Award, 2021 Facebook faculty award, 2020 Australia's Most Innovative Engineer Award and a ACM distinguished speaker. I am also a Lawrence Scholar and a recipient of Paul E. Torgersen Excellent research award, a 2018 DOE pathway to excellence research award, 2015 and 2017 DOE PNNL lab outstanding research award, two Supercomputing (IEEE/ACM SC) best paper runners-up (2015 and 2017), and 2017 HiPEAC paper award. I have published in the top HPC and computer architecture conferences including ISCA, HPCA, ASPLOS, MICRO, and Supercomputing (SC). My past and current research has been supported by Microsoft, Google, NVIDIA, Intel, U.S. government agencies including DOE office of science (ASCR), DoD, DARPA and DoE Lab LDRD, and Australian Research Council (ARC). During my tenure at PNNL, I led two DOE lab LDRD projects on AI-driven architecture design and large-scale data analytics acceleration. At University of Sydney, I run Future System Architecture (FSA) Lab with my wonderful students. Currently, we are actively working with our collaborators from UW Seattle, UT Austin, NYU, Google Brain, Facebook Reality Lab and Alibaba Research. In my spare time, I am also consulting for tech startups.

I do research at the boundary of system software and hardware, breaking down abstraction barriers, and rethinking the hardware–software interface. I have a particular interest of holistic system design and software-hardware co-design. More broadly, my expertise lies in the general areas of computer system architecture and high performance computing (HPC). I hold the strong belief that future beyond Moore’s system architectures will become increasingly heterogeneous which demands new software (programming system, compiler, runtime) and hardware design paradigm to accommodate such complex many-accelerator integrated systems. As a computer system researcher, I am inspired to push the concept of co-design to create efficient and scalable solutions for emerging systems and applications, including future planet-scale Extended-Reality (XR) system, System ML and AI-driven System Designs, and even future quantum accelerator based heterogeneous architectures., In the recent years, with my amazing students and collaborators, we have published some of the first papers (HPCA'17, HPCA'18, HPCA'19, ISCA'19, ASPLOS'21, HPCA'23) on future VR system characterizations and system-level design & optimizations (including both multi-accelerator based HMD SoC and cloud server designs) in the field of computer architecture. Additionally, my recent work in industry research on machine learning system optimizations and scalability are being delopyed to real-world large-scale enterprise usage for millions of users.

Research Interests

• Hardware-Software System Co-design
• Emerging architectures and systems (e.g., heterogenous architectures, emerging many-core accelerators, novel memory technologies and quantum architectures)
• High Performance Computing (HPC)
• Machine Learning System and SystemML
• Metaverse System Design: designing future planet-scale XR system and exploring its efficiency, scalability, quality of experience and social impact.

Some Recent Research Highlights (Full Publication List;FSA Group)

[OSDI'24] "MonoInfer: Enabling a New Monolithic Optimization Space for Neural Network Inference Tasks on Modern GPU-Centric Architectures"  Donglin Zhuang, Zhen Zheng, Haojun Xia, Xiafei Qiu, Junjie Bai, Wei Lin, Shuaiwen Leon Song, accepted to appear in OSDI 2024.

[VLDB'24] "Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity"  Haojun Xia, Zhen Zheng, Yuchao Li, Donglin Zhuang, Zhongzhu Zhou, Xiafei Qiu, Yong Li, Wei Lin, Shuaiwen Leon Song, accepted to appear in VLDB 2024.

[Arxiv] "Chiplet Cloud: Building AI Supercomputers for Serving Large Generative Language Models"  Huwan Peng, Scott Davidson, Richard Shi, Shuaiwen Leon Song, Michael Taylor.

[Arxiv] "DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales"  Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zhongzhu Zhou, Michael Wyatt, Molly Smith, Lev Kurilenko, Heyang Qin, Masahiro Tanaka, Shuai Che, Shuaiwen Leon Song, Yuxiong He.

[SC'23] "Mitigating Coupling Map Constrained Correlated Measurement Errors on Quantum Devices"  Alan Robertson, Shuaiwen Leon Song, accepted to appear in IEEE/ACM Supercomputing'23.

[HPCA'23] "Post0-VR: Enabling Universal Realistic Rendering for Modern VR via Exploiting Architectural Similarity and Data Sharing"  In the 29th IEEE International Symposium on High-Performance Computer Architecture.

[EuroSys'23] "TEA: A General-Purpose Temporal Graph Random Walk Engine"  In European Conference on Computer Systems (2023).

[MLSys'22] "Randomness In Neural Network Training: Characterizing The Impact of Tooling"  In Machine Learning and Systems (MLSys) 2022. (With Google Brain, Acceptance rate 20.6%) Artifact.

[ASPLOS'22] "AStitch: Enabling A New Multi-Dimensional Optimization Space for Memory-Intensive ML Training and Inference on Modern SIMT Architectures"  In the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS'22, with Alibaba Research).

[VLDB'22] "COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression"  the 48th International Conference on Very Large Data Bases (VLDB'22).

[PPoPP'22] "Vapro: Performance Variance Detection and Diagnosis for Production-Run Parallel Applications"  ACM Principles and Practice of Parallel Programming (PPoPP). The journal versioin is published at TPDS.

[ICS'22] "Bring Orders into Uncertainty: Enabling Efficient Uncertain Graph Processing via Novel Path Sampling on Multi-Accelerator Systems"  The International Conference on Supercomputing (ICS).

[ICDE'22] "TeGraph: A Novel General-Purpose Temporal Graph Computing Engine"  IEEE International Conference on Data Engineering (ICDE).

[Supercomputing'21] "Dr. Top-k: Delegate-Centric Top-k on Heterogenous HPC Architectures"  The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'21). Code Reproductivity

[Supercomputing'21] "MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers"  The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'21). Code Reproductivity

[MICRO'21] "Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving"  IEEE/ACM International Symposium on Microarchitecture 2021 (MICRO'21). Artifact

[ESEC/FSE] "Toward Efficient Interactions between Python and Native Libraries"  The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2021.

[TC] "MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors"  IEEE Transactions on Computers.

[ICS'21] "ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning"  The International Conference on Supercomputing (ICS).

[ASPLOS YArch'21] "Quantum Von Neumann Architectural Modeling for Algorithm Analysis"  The third Young Architect Workshop (YArch) of ASPLOS'21. talk video.

[ISCA'21] "η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities"  Xingyao Zhang, Haojun Xia, Donglin Zhuang, Hao Sun, Xin Fu, Michael Taylor, Shuaiwen Leon Song, The International Symposium on Computer Architecture [ISCA'21]. (Acceptance rate: 18.7%). talk

[TC] "Enabling Highly Efficient Capsule Networks Processing Through Software-Hardware Co-Design"  Xingyao Zhang, Xin Fu, Donglin Zhuang, Chenhao Xie, Shuaiwen Leon Song, IEEE Transactions on Computers (TC), [A special issue on research highlights of Computer Architecture for 2019-2020.]

[ASPLOS'21] "Q-VR: System-Level Design for Future Collaborative Virtual Reality"  Chenhao Xie, Xie Li, Yang Hu, Huwan Peng, Michael B. Taylor, Shuaiwen Leon Song, The 26th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS'21). (Acceptance rate: 17.5%). This is the first work in the computer architecture community on a possible design for future planet-scale mobile VR systems with low latency and high quality. We transform a complex multi-dimensional problem into a system design problem, providing real-world observations, practical insights and a software-hardware co-design solution.

[HPCA'20] "Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design" 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA), (Acceptance rate: 19.4%; This work tries to start some conversation on how we deal with these new types of hybrid network structures where not only 2D or 3D convolution poses as the most significant efficiency block. We are helping CapsuleNet actually scale and become hardware-friendly (via Processing-in-Memory).) Slides

[ISCA'19] "OO-VR: NUMA friendly object-oriented VR rendering framework for future NUMA-based multi-GPU systems" Chenhao Xie, Xin Fu, Mingsong Chen, Shuaiwen Leon Song. In Proceedings of the 46th International Symposium on Computer Architecture (ISCA '19).  (Acceptance rate: 16.9%; Investigating into what future chiplet-based cloud server system will look like to reach scalable parallel rendering efficiency via a new hardware-software co-design framework.) Viedo

[HPCA'19] "PIM-VR: Erasing Motion Anomalies In Highly-Interactive Virtual Reality World with Customized Memory Cube" Chenhao Xie, Xingyao Zhang, Ang Li, Xin Fu, Shuaiwen Leon Song. In 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA).  (Acceptance rate: 19.7%; One of the first papers in the community identifying that state-of-the-art software-level reprojection optimization for commerical VR devices involuntarily causes significant hardware-level bottlenecks and motion anomalies.) Viedo

[Supercomputing'19] "BSTC: a novel binarized-soft-tensor-core design for accelerating bit-based approximated neural nets" Ang Li, Tong Geng, Tianqi Wang, Martin C. Herbordt, Shuaiwen Leon Song, Kevin J. Barker. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '19). (Acceptance rate: 20.9%; Introducing the concept of software-based tensorcore for bit-level parallelism on manycore accelerators) Viedo

[PPoPP'18] "Superneurons: dynamic GPU memory management for training deep neural networks" Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Tim Kraska. In Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '18). (Acceptance rate: 20%; GPUs'memory are too small to have balanced efficiency matching with powerful cores when handling training for large non-linear networks. SuperNeurons helps users tackle this issue.)Viedo

[HPCA'18] "Perception-Oriented 3D Rendering Approximation for Modern Graphics Processors." Chenhao Xie, Xin Fu, Shuaiwen Leon Song. In 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA).  (Acceptance rate: 19.7%; Connecting architectural bit stream and its manipulation to user perception impact.) Viedo

[CGO'18] "CUDAAdvisor: LLVM-based Runtime Profiling for Modern GPUs." Du Shen, Shuaiwen Leon Song, Ang Li, Xu Liu.  In ACM International Symposium on Code Generation and Optimization (CGO).

[ICS'18] "Warp-Consolidation: A Novel Execution Model for GPUs." Ang Li, Weifeng Liu, Linnan Wang, Kevin Barker, Shuaiwen Leon Song.  In 32nd ACM International Conference on Supercomputing (ICS'18). (A new GPU programmming model for sychronizaton-critical applications).

[TACO] "NUMA-Caffe: NUMA-Aware Deep Learning Neural Networks." Probir Roy, Shuaiwen Leon Song, SRIRAM KRISHNAMOORTHY, Dipanjan Sengupta, Xu Liu.  In ACM Transactions on Architecture and Code Optimization (TACO).

[TPDS] "Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect."  Ang Li, Shuaiwen Leon Song, Jieyang Chen, Jiajia Li, Xu Liu, Nathan R. Tallent, Kevin J. Barker.   In IEEE Transactions on Parallel and Distributed Systems (TPDS). [IISWC'18, Best Paper Finalist] "Tartan: Evaluating Modern GPU Interconnect via a Multi-GPU Benchmark Suite."  Ang Li, Shuaiwen Leon Song, Jieyang Chen, Jiajia Li, Xu Liu, Nathan R. Tallent, Kevin J. Barker.  In 2018 IEEE International Symposium on Workload Characterization (IISWC).

[Supercomputing'17, Best Paper Finalist, DoE Research Highlight article] "Exploring and analyzing the real impact of modern on-package memory on HPC scientific kernels." Ang Li, Weifeng Liu, Mads Ruben Burgdorff Kristensen, Brian Vinter, Hao Wang, Kaixi Hou, Andres Marquez, Shuaiwen Leon Song. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '17).  (Acceptance rate: 61/327=18.6%; First research investigating performance impact of introducing on-package memory into memory heriachy on a large spectrum of fundamental HPC scientific kernels and provide formalized modeling scheme for easy performance analysis at large scale. It recieves SC'17 best paper finalist and DoE research highlight.)

[MICRO-50] "BVF: enabling significant on-chip power savings via bit-value-favor for throughput processors." Ang Li, Wenfeng Zhao, Shuaiwen Leon Song. In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO '17).  (Acceptance rate:18.6%).

[ASPLOS-17, HiPEAC paper award]"Locality-Aware CTA Clustering for Modern GPUs." Ang Li, Shuaiwen Leon Song, Weifeng Liu, Xu Liu, Akash Kumar, Henk Corporaal. In Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '17). (Acceptance rate:17.4%) Viedo

[HPCA'17]"Processing-in-Memory Enabled Graphics Processors for 3D Rendering." Chenhao Xie, Shuaiwen Leon Song, Xin Fu. In n 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA).  (Acceptance rate:21.6%; Using processing-in-memory technique along with graphics software pipeline re-design to accelerate modern rendering efficiency. )

[ICS'16] "SFU-Driven Transparent Approximation Acceleration on GPUs." Ang Li, Shuaiwen Leon Song, Mark Wijtvliet, Akash Kumar, Henk Corporaal.  In 30th ACM International Conference on Supercomputing (ICS). (Interesting concept about approximation units on modern many-core architectures and how they collaborate together in system to achieve dynamic performance-accuracy trade-offs.)

[HPDC'16] "SMT-Aware Instantaneous Footprint Optimization."  Probir Roy, Xu Liu, Shuaiwen Leon Song.  In 25th ACM international Symposium on High-Performance and Distributed Computing (HPDC). (Identifying, debugging and fixing false sharing on multicore processors.)

[ICS'15] "Locality-Driven Dynamic GPU Cache Bypassing."  Chao Li, Shuaiwen Leon Song, Hongwen Dai, Albert Sidelnik, Siva Hari, Huiyang Zhou  In 29th ACM International Conference on Supercomputing (ICS).

[Supercomputing'15, Best Paper Finalist]"GraphReduce: processing large-scale graphs on accelerator-based systems." Dipanjan Sengupta, Shuaiwen Leon Song, Kapil Agarwal, Karsten Schwan. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '15).  (Acceptance rate:18.5%)

Recent Awards and Recognition

• 2022 Alibaba AIR Faculty Award
• 2022 SOAR Fellowship Award
• 2021 Facebook Faculty Award
• 2021 Google GCP Award for Google Brain collaboration.
• ACM distinguished speaker
• 2020 Australia's Most Innovative Engineer Award
• 2020 Google collaboration resource award for Machine Learning System research
• 2018 U.S. DOE Pathway to Excellence Research Award
• 2018 U.S. Department of Energy Research Spotlight
• 2018 Best paper finalist for IISWC
• 2017 IEEE Early Career Award in High Performance Computing (IEEE/ACM 高性能计算杰出新人奖. I am the first winner of the award with Chinese background).
• Invited judge for U.S. DOE R&D 100 award in 2017 and 2018
• 2017 HiPEAC Paper Award
• 2017 Best paper runner-up for IEEE/ACM Supercomputing (SC'17)
• 2015 Best paper runner-up for IEEE/ACM Supercomputing (SC'15)
• 2016 and 2017 U.S. DOE PNNL lab Outstanding Performance Award
• Paul E. Torgersen excellent research award
• Lawrence Scholar

Professional Services and Activities

Organizing Committee

• Area chair: Architecture and Networks,Supercomputing (SC) 2022
• Area chair: Architecture (IPDPS 2021)
• Area chair: Performance (ICPP2020)
• Best paper selection panel chair: IPDPS21
• ACM ASPLOS'18, chair for poster and ACM student research compeition (SRC)
• Publicity chair ,ACM ICS'22
• Publicity Chair, ACM HPDC, 2016, 2017, 2018 and 2019
• Publication chair, 2018 ACM International Conference on Supercomputing (ICS)
• Workshop chair and steering committee, International Workshop on Emerging Parallel and Distributed Runtime Systems and Middleware (Supercomputing)
• Workshop chair, International workshop on High-Performance, Power-Aware Computing (HPPAC)

Journal Editorial

• Editor, Elsevier High-Confidence Computing.
• Associate editor: IEEE transaction on sustainable computing.
• Reivew Board: IEEE Transactions on Parallel and Distributed Systems (TPDS)
• Review Board: Journal for Concurrency and Computation, Practice and Experience (CCPE)

Recent Program Committee

• PC, the 50th IEEE/ACM International Symposium on Computer Architecture (ISCA), 2023
• PC, Neural Information Processing Systems (NIPS) 2023.
• PC, ACM SIGPLAN Annual Symposium Principles and Practice of Parallel Programming (PPoPP),2023
• PC, 54th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2022
• ERC, 49th International Symposium on Computer Architecture (ISCA), 2022
• Session chair, 48th International Symposium on Computer Architecture (ISCA), 2021
• ERC, 54th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2021
• PC, 48th International Symposium on Computer Architecture (ISCA), 2021
• PC, 2021 IEEE/ACM Supercomputing (SC)
• PC, ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2021
• ERC, 27th IEEE international symposium on High-Performance Computer Architecture (HPCA),2021
• ERC, IEEE/ACM International Symposium on Microarchitecture (MICRO-53), 2020
• ERC, 47th International Symposium on Computer Architecture (ISCA), 2020
• PC, ACM SIGPLAN Annual Symposium Principles and Practice of Parallel Programming (PPoPP), 2019˜2020
• PC, ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2016˜2018, 2020, 2021
• PC, IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2015˜2017
• PC, IEEE/ACM International Conference on Parallel Architectures and Compilation Techniques (PACT), 2019
• PC,ACM International Conference on Supercomputing (ICS), 2017,2021
• PC, IEEE International Conference on Distributed Computing Systems (ICDCS), 2020
• PC, IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2016˜2018,2020,2021
• NSF proposal panelist, 2015
• R&D award judge, 2018˜2019

Journal Reviewer

• ACM Transactions on Computer Systems (TOCS), 2020
• ACM Transactions on Architecture and Code Optimization (TACO), 2018
• IEEE Transactions on Parallel and Distributed Systems (TPDS), 2014˜2020
• IEEE Transactions on Computers (TC), 2015˜2016
• IEEE Transactions on Sustainable Computing, 2019˜2020
• Journal of Parallel and Distributed Computing - Elsevier (JPDC)
• The International Journal of High Performance Computing (IJHPCA)
• The Journal of Supercomputing Elsevier (JOS)
• Parallel Computing-Elsevier (ParCo)

Recent Talks

• 2023 DeepSpeed4Science global announcement [WinBuzzer] [Metaverse Post] [Microsoft Research Blog] [今日头条] [知乎]
• 2023 Keynote speaker for IPDRM workshop at Supercomputing'23
• 2023 Keynote speaker for TIES workshop@PEARC'23
• 2023 Opening keynote for DoE MODSIM workshop 2023 at University of Washington
• "Designing Future Planet-Scale XR System”, UW Seattle CSE guest lecture (April 2021), AMD Research Asia, Future System Research Trends @HPDC'2021, Monash University Engineering Society (2020), Digital Science Initiative (2020), Googel Research (Feb 2020) and Rice ECE (August 2019).
• "Unleashing the Power of Holistic System Design for Modern Emerging Workloads", Intel Research, University of Virginia, U of Minnesota, UC Irvine (ECE), U of Sydney, U of Pittsburgh, U of Connecticut, Rutgers U (ECE), UC Riverside, NUS, 2019.
• "Designing Future Non-Von Neumann Architectures for Big Data Analytics", multi-agency review, DOE/DOD HPDA project, 2018 and 2019.
• Invited to Microsoft Research Faculty Summit 2018.
• “Advanced HPC System Research: State-of-the-Practice and Future Roadmap”, invited Lecture, Bio-engineering Department, Duke University, Nov 2018.
• “Exploring and Analyzing the Real Impact of Modern On-Package Memory on HPCScientific Kernels”, conference talk, Supercomputing’18, Denver, Colorado, Nov 15th, 2017.
• “Binarzied Software Tensor Core”, invited talk, Microsoft Research, Redmond, WA, Nov 3rd, 2017.
• “Software-Hardware Co-Design for Future Complex HPC Architectures: DOE perspectives”, invited talk, Intel Research Lab, Hillsboro, OR, Oct 30th, 2017.
• Invited to Microsoft Research Faculty Summit 2017.
• “Whither Advanced GPU Research in HPC? Where We Are, Where We are Going”, Invited Speaker, Tsinghua University, CS department; Peking University, ECE department; Chinese Academy of Science, Institute of Computing Technologies, April 14th, 2017.
• “Locality-Aware CTA Clustering For Modern GPUs", conference talk, ACM ASPLOS’17, Xi’An, China, April 2017.

Publicity

• 2022 IEEE mid-career award for Scalable Computing.
• 2022 Google Brain Collaboration award.
• 2022 AIR Faculty Award
• SOAR Faculty Fellowship Award.
• ACM distinguished speker.
• 2021 Facebook Faculty Award.
• 2021 Google Brain Colalboration Award.
• 2020 Australia's Most Innovative Engineer Award; University of Sydney News: https://www.sydney.edu.au/news-opinion/news/2020/07/06/engineers-australia.html.
• DOE featured research highlight: [Check the news!]
• Digital Trends (Boosting graphics performance through processing in-memory): [Check the news!]
• Yahoo Tech: [Check the news!]
• Bit-Tech: [Check the news!]
• PNNL featured research news: http://www.pnnl.gov/news/release.aspx?id=4385
• PNNL research highlight: Changing the game, http://www.pnnl.gov/science/highlights/highlight.asp?id=4495
• PNNL research spotlight: Improving computing system performance, http://www.pnnl.gov/science/highlights/highlight.asp?id=4238, March 2016.
• "Powering Down", article about my work on power management on large-scale system, published on DOE DEIXIS magazine featured article, [Check the news!], written by Monte Basgall.
• PNNL Science Research Highlight: Energy Star: Novel models of HPC systems depict the interplay between energy efficiency and resilience", Link: http://www.pnnl.gov/science/highlights ,2015.
• PNNL ACMDD staff award and honors: PNNL HPC Staff Take on Energy E.ciency, Resilience at scale", Link: http://www.pnnl.gov/science/highlights, 2015.
• PNNL ACMDD staff award and honors: PNNL HPC Staff research: Improving Energy, Performance Efficiency for High Performance Computing", Link: http://www.pnnl.gov/science/highlights, 2015.
• Current Magazine: \HPC system modeling: Depicting interplay between energy efficiency and resilience", June 2015 issue.
• InsideHPC: "PNNL looks at undervolting to meet exascale goals", written by Rich Bruecker: [Check the news!]
• HPC Wire Top Feature Article: "Tackling the Power and Energy wall for Future HPC Systems", [Check the news!], Dec, 2013.

Current Students and Alumni

• Please check our group site at FSA-Lab

Past and Current Sponsors and Collaborators