Welcome back and celebrate MICRO!
Welcome back and celebrate MICRO!

Abe, Jinyong and Kuan-Chieh are back from their internships. Yu-Ching also got his first MICRO paper in!

ESCAL @ FMS 2019
ESCAL @ FMS 2019

ESCAL made its debut at FMS 2019! Yu-Ching and Yu-Chia presented a poster on intelligent SSDs for machine learning. Hung-Wei also gave a talk in the intelligent SSD session.

ESCAL made its debut at MLB!
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 got a nice office at UCR
ESCAL got a nice office at UCR

As UCR committed more resources to our growth, ESCAL is now at UCR’s new office space. A physical therapist, Chi-Lun Chiao also came to help us adjusting our new standing desks to make sure that everyone can be productive but also healthy!

ESCAL @ MICRO 2019
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!

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.


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.


People

Faculty

Graduate students

Jinyoung Choi

Alumni

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

Publications