Hung-Wei Tseng

Assistant Professor, University of California, Riverside

I am currently an assistant professor in the Department of Electrical and Computer Engineering and a cooperating faculty of the Department of Computer Science and Engineering at University of California, Riverside. I am now leading the Extreme Storage & Computer Architecture Laboratory. I received Facebook Research Award, 2018. Prior to joining UCR, I served as an assistant professor for the Department of Computer Science and the Department of Electrical and Computer Engineering at NC State University . I was a PostDoc of the Non-volatile Systems Laboratory and a lecturer of the Department of Computer Science and Engineering at University of California, San Diego with Professor Steven Swanson. My thesis work with Professor Dean Tullsen is data-triggered threads. This work was selected by IEEE Micro "Top Picks from Computer Architecture" in 2012.

If you would like to take CS202 Winter 2020, please fill in this form using your campus gmail account before sending me an e-mail of requesting for permission. All requests without a submission of this form will be ignored.

Research Projects

Intelligent Data Storage

We built Intelligent Data Storage Systems, including Summarizer and Morpheus, to demonstrate the potential of exposing the processing power inside the controllers of modern non-volatile storage devices. The resulting systems demonstrate significant speedups in GPGPU applications, database systems, and the potential of machine learning applications. (More)

Efficient Heterogeneous Computers for Big-Data Applications

As the slowdown of Moore's Law and the discontinuation of Dennard Scailing, big-data applications (e.g. machine learning, data analytics, scientific computing, and etc.,.) must rely on heterogeneous computing units, including GPUs, FPGAs or ASICs as well as heterogeneous data storage, including DRAM, NVRAM, flash memory, to complete their own tasks. We built systems that make more efficient use of heterogeneous system components to accelerate applications. The resulting system can accelerate (More)

Machine Learning Assisted Data Storage

With the advance of machine learning techniques, many heurestic-based mechanisms can potentially be replaced by machine-learning models. We demonstrated that an machine-learning assisted SSD can extend its life time by 17%, without modifications and additional hints from the software systems but adding zero costs to existing storage devices. (More)

Next-Generation Wireless Interconnects

Next-generation wireless technologies can obtain more than 5Gbps bandwidth per-link. We designed and optimized systems using next-generation wireless links to replace traditional wired link. We focus on improving the latency and system overhead to deliver competitive performance for applications comparing with using wired links.

Intelligent Data Storage

As parallel computer architectures significantly shrinking the execution time in compute kernels, the performance bottlenecks of applications shift to the rest of part of execution, including data movement, object deserialization/serialization as well as other software overheads in managing data storage. To address this new bottleneck, the best approach is to not move data and endow storage devices with new roles.

Morpheus is one of the very first research project that implements this concept in real systems. We utilize existing, commercially available hardware components to build the Morpheus-SSD. The Morpheus model not only speeds up a set of heterogeneous computing applications by 1.32x, but also allows these applications to better utilize emerging data transfer methods that can send data directly to the GPU via peer-to-peer to further achieve 1.39x speedup. Summarizer further provides mechanisms to dynamically adjust the workload between the host and intelligent SSDs, making more efficient use of all computing units in a system and boost the performance of big data analytics. This line of research also helps Hung-Wei's team receive Facebook research award, 2018.

Building Efficient Heterogeneous Computers

As the discontinuation of Dannard scaling and Moore's Law, computers become heterogeneous. However, moving data among heterogeneous computing units and storage devices becomes an emerging bottleneck in these systems.

My research proposes the "Hippogriff" system that revisits the programming model of moving data in heterogeneous computer systems. Instead of using the conventional CPU-centric, programmer-specified methods, the Hippogriff system simplifies the application interface and provide a middle layer to efficiently handle the data movement. We also implemented peer-to-peer data transfer between the GPU and the SSD in the Hippogriff system.

The preliminary result demonstrates 46% performance gain by applying Hippogriff to a set of rodinia GPU applications. For highly optimized GPU MapReduce framework, Hippogriff still demonstrates up to 27% performance gain.

  • Yu-Ching Hu, Murtuza Lokhandwala, Te I and Hung-Wei Tseng. Dynamic Multi-Resolution Data Storage. In the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2019
  • Jing Li, Hung-Wei Tseng, Chunbin Lin, Steven Swanson, and Yannis Papakonstantinou. HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics. Proceedings of VLDB Endowment, Volume 9(14), 2016.
  • Yang Liu, Hung-Wei Tseng, Mark Gahagan, Jing Li, Yanqin Jin and Steven Swanson. Hippogriff: Efficiently Moving Data in Heterogeneous Computing Systems. In 34th IEEE International Conference on Computer Design (ICCD 2016). Oct. 2016.
  • Yang Liu, Hung-Wei Tseng and Steven Swanson. SPMario: Scale Up MapReduce with I/O-Oriented Scheduling for the GPU. In 34th IEEE International Conference on Computer Design (ICCD 2016). Oct. 2016.
  • Hung-Wei Tseng, Yang Liu, Mark Gahagan, Jing Li, Yanqin Jin, and Steven Swanson. Gullfoss: Accelerating and Simplifying Data Movement among Heterogeneous Computing and Storage Resources . Department of Computer Science and Engineering, University of California, San Diego technical report technical report CS2015-1015, 2015.

Machine Learning Assisted Data Storage

The advancement of machine learning techniques enables more accurate predictions, data classifications and lead to improved decision making. This is especially helpful for dealing with system design issues that traditionally rely on heuristics. In this project, we use machine learning models to replace traditional heuristic-based mechanisms to better assist the management of storage systems. The initial result shows 19% extension in SSD lifetime without adding any hardware cost.

  • Te I, Murtuza Lokhandwala, Yu-Ching Hu, and Hung-Wei Tseng. Pensieve: a Machine Learning Assisted SSD Layer for Extending the Lifetime. In IEEE International Conference on Computer Design (ICCD 2018). October, 2018.
  • Te I, Yu-Ching Hu, Murtuza Taher Lokhandwala, and Hung-Wei Tseng. Extending SSD Lifetime with Machine Learning. The 9th Non-Volatile Memories Workshop, NVMW 2018, 2018.

Publications

(Full listing)

Smart Storage Systems

Application/Storage Systems Interaction

  • Te I, Murtuza Lokhandwala, Yu-Ching Hu, and Hung-Wei Tseng. Pensieve: a Machine Learning Assisted SSD Layer for Extending the Lifetime. In IEEE International Conference on Computer Design (ICCD 2018). October, 2018.
  • Jing Li, Hung-Wei Tseng, Chunbin Lin, Steven Swanson, and Yannis Papakonstantinou. HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics. Proceedings of VLDB Endowment, Volume 9(14), 2016.
  • Yang Liu, Hung-Wei Tseng, Mark Gahagan, Jing Li, Yanqin Jin and Steven Swanson. Hippogriff: Efficiently Moving Data in Heterogeneous Computing Systems. In 34th IEEE International Conference on Computer Design (ICCD 2016). Oct. 2016.
  • Yang Liu, Hung-Wei Tseng and Steven Swanson. SPMario: Scale Up MapReduce with I/O-Oriented Scheduling for the GPU. In 34th IEEE International Conference on Computer Design (ICCD 2016). Oct. 2016.

Data-triggered threads

Non-volatile Storage Systems

Computer science education

Wireless Networks

Software

Advising & Join

Graduate Students

I am currently advising the following top-notch graduate students:

Undergraduate Students

I also work with the following talented undergraduate students:
  • Timotius Oentung

Alumni

I have also advised these students, who have each graduated:
  • Zecao Lu (C.S., M.S., NC State University, 2019. Interning at Amazon)
  • Xindi Li (C.S., M.S., NC State University, 2018. Now at Bloomberg)
  • Chao Huang (C.S., M.S., NC State University, 2018. Now at Amazon)
  • Zackary Allen (C.S., B.S., NC State University, 2018. Now at Red Hat)
  • Alec Rohloff (C.S., B.S., NC State University, 2018.)
  • Te I (C.S., M.S., NC State University, 2018. Now at Google)
  • Vaibhava Lakshmi (ECE, M.S., NC State University, 2018. Dell EMC)
  • Murtuza Taher Lokhandwala (ECE, M.S., NC State University, 2018. Apple)
  • Mahesh Bonagiri(ECE, M.S., NC State University, 2018. Nvidia)
  • Joshua Okrend

For prospects

Developing awesome ideas and training researchers are my duties as a professor. I am always looking for new graduate students. If you are interested at working with me, please apply to either Department of Electrical and Computer Engineering (Preferred) or the Department of Computer Science and Engineering of University of California, Riverside and mention me as a potential advisor in the application system.

Teaching

Present

Upcoming

Prior Courses

News

  • Our paper, Dynamic Multi-Resolution Data Storage, is accepted by MICRO 2019.
  • Our paper, Pensieve: a Machine Learning Assisted SSD Layer for Extending the Lifetime, is accepted by ICCD 2018.
  • My NSF proposal, CSR: Small: IOQL: an I/O Interface for Near-Data Processing , is awarded! Thanks NSF!
  • Hung-Wei received Facebook Research Award, 2018.
  • Yu-Ching presented his work during the poster session of Non-Volatile Memory Workshop, 2018
  • I am invited to join the organization committee as a registration chair of ISCA, 2018 (The 45th International Symposium on Computer Architecture)
  • I am invited to join the organization committee as a web chair of ASPLOS, 2018 (The 23rd ACM International Conference on Architectural Support for Programming Languages and Operating Systems)
  • Our paper with Murali's team at USC, "Summarizer: Trading Bandwidth with Computing Near Storage", is accepted by MICRO 2017!
  • My NSF proposal, CRII: CSR: Rethinking the FTL in SSDs -- a file translation layer instead of a flash translation layer, is awarded!
  • My paper with Dr. Steven Swanson's team at UCSD, KAML: A Flexible, High-Performance Key-Value SSD, is accepted by HPCA 2017!
  • I am invited to join the organization committee as a web chair of HPCA, 2017 (The 23rd IEEE Symposium on High Performance Computer Architecture)
  • I am invited to join the organization committee as a web chair of MICRO, 2016 (The 49th Annual IEEE/ACM International Symposium on Microarchitecture)