ESCAL proudly releases OpenUVR
ESCAL proudly releases OpenUVR
OpenUVR is an open-source, open-architecture VR system! The paper is accepted by the 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2021). You may access the preprint in arXiv
You can now start building your OpenUVR-based VR system by clone our GitHub repo at

The work is done together with ESCAL’s former undergraduate students, Alec Rohloff, Zack Allen, Joshua Okrend at NC State,

Leo Lin, our high-school lab intern who is now an UC Berkeley undergraduate student,
Eric Nie, ESCAL’s former undergraduate student at UCR, and Yu-Chia Liu.
Varifocal Storage chosen as IEEE Micro Top Picks!
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.

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!

Welcome Andrew and best wishes to Eric and Leo!
Welcome Andrew and best wishes to Eric and Leo!

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!

ESCAL’s presence in HPCA/PPoPP/CGO 2020
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!

Extreme Storage & Computer Architecture Laboratory

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 and MICRO TopPicks in 2020.

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.

Optimizing the I/O system software stack for emerging applications

With hardware accelerators improving the latency in computation, the system software stack that were traditionally underrated in designing applications becomes more critical. In ESCAL, we focus on those underrated bottlenecks to achieve significant performance improvement without using new hardware. The most recent example is the OpenUVR system, where we eliminate unnecessary memory copies and allow the VR system loop to complete within 14 ms latency with just modern desktop PC, existing WiFi network links, raspberry Pi 4b+ and an HDMI compatible head mount display.



Graduate students

Yunan Zhang


Xindi Li (C.S., M.S., 2018. Now at Bloomberg)

Chao Huang (C.S., M.S., 2018)

Zackary Allen (C.S., B.S., 2018. Now at LexisNexis)

Alec Rohloff (C.S., B.S., 2018.)

Te I (C.S., M.S., 2018. Now at Google)

Vaibhava Lakshmi (ECE, M.S., 2018. Dell EMC)

Murtuza Taher Lokhandwala (ECE, M.S., 2018. Apple)

Mahesh Bonagiri(ECE, M.S., 2018. Nvidia)

Joshua Okrend

Stefan O’Neil