Though every commencement is going virtual, we are still glad to the move of our students! Two of our lab alumni, Chengyi “Eric” Nie and Kung-Min “Leo” Lin, will begin a new chapter of their lives! Chengyi “Eric” Nie completed his B.S. degree at UC Riverside and will become a PhD student at Stony Brook University this fall! Kung-Min “Leo” Lin interned at ESCAL last summer as a high school student is going to be a freshman at UC Berkeley this fall! In the meantime, we also want to welcome our new PhD student, Yunan “Andrew” Zhang, who just got his B.S. degree in Computer Science from UC San Diego (where is also Hung-Wei, Yu-Ching and Yu-Chia’s alma mater) in joining us this fall!
Varifocal Storage chosen as IEEE Micro Top Picks!
Our paper, Dynamic Multi-Resolution Data Storage, authored by Yu-Ching Hu and Hung-Wei Tseng, two former ESCAL students, Murtuza Lokhandwala and Te I, has been selected for IEEE Micro’s Top Picks from the 2020 Computer Architecture Conferences. Top Picks is an annual special edition of IEEE Micro magazine that acknowledges the 10-12 most significant research papers. The authors found a smarter use of in-storage processors and present a holistic system design to address the emerging I/O and data preprocessing bottleneck in modern heterogeneous computer architectures based on approximate hardware. Prior to the recognition from Micro Top Picks, the same project also received ACM/IEEE MICRO’s best paper honorable mention in 2019 and Facebook’s research award in 2018.
ESCAL made its debut at MLB!
ESCAL visited Angels stadium on 9/14 — the first time ESCAL get together and attend an MLB game. It’s also the first time three of the group visiting an MLB game. This game also features a firework show — apparently the most exciting part during this visit.
ESCAL’s presence in HPCA/PPoPP/CGO 2020
The ESCAL group were all out-of-town to San Diego, CA during the period of HPCA/PPoPP/CGO 2020 in supporting the volunteering team together with other students from UCR, UCSD and Stony Brook University. Many attendees and organization committee members expressed that “the conference is surprisingly smooth with these volunteers”. Well done!
ESCAL @ MICRO 2019
Yu-Ching Hu presented his paper “Dynamic Multi-Resolution Data Storage”, co-authored with Murtuza Lokhandwala, Te I and Hung-Wei Tseng in the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2019. The talk/poster is well-received. This paper is nominated as a best paper candidate and got “honorable mention”, right behind two best papers at the end. Well done, Yu-Ching!
With the rapid growth of dataset sizes but limited improvement of high-performance computers, we need to revisit the existing programming and execution models to efficiently utilize all system components. In modern computers, lots of deficiencies in applications are related to data management and movements. The vision of Extreme Storage & Computer Architecture Laboratory is to revolutionary change the way how people think about programming and computing today — using a data-centric perspective in programming instead of the conventional computing-centric approach. ESCAL conducts research in systems and computer architecture with focus on storage systems, parallel processing, high-performance computing, programming languages and runtime systems.
Research Projects
Building intelligent data storage & I/O devices
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 ESCAL receive Facebook research award, 2018.
Efficient storage system for heterogeneous servers
Although high-performance, non-volatile memory technologies and network devices significantly improve the speed of supplying data to heterogeneous computing units, the performance of these devices are still far behind the capabilities of heterogeneous computing units. For example, modern SSDs can read more than 3GB of data per second, but GPUs can process more than 17GB of data for database aggregation operations within the same period of time. As result, the heterogeneous computing units are under-utilized. We will revisit the design of existing runtime systems to transparently improve the utilization of system components, potentially leading to speedup or better energy-efficiency.
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.
Next-generation wireless technologies and storage systems
As networking becomes a popular interface for storage systems, we see the demand of optimizations across the boundary of conventional storage system and network stacks. ESCAL focuses on storage systems attaching to next-generation wireless technologies that 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.
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
Kiran Kumar Matam, Gunjae Koo, Haipeng Zha, Hung-Wei Tseng and Murali Anavarum. GraphSSD: Graph Semantics Aware SSD. In the 46th International Symposium on Computer Architecture, ISCA 2019.
Gunjae Koo, Kiran Kumar Matam, Te I, Hema Venkata Krishna Giri Narra, Jing Li, Steven Swanson, Murali Annavaram, and Hung-Wei Tseng. Summarizer: Trading Bandwidth with Computing Near Storage. In 50th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2017
Yanqin Jin, Hung-Wei Tseng, Steven Swanson and Yannis Papakonstantinou. KAML: A Flexible, High-Performance Key-Value SSD. In 23rd International Symposium on High Performance Computer Architecture (HPCA 2017). February 2017.
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.
