About Me
I am a Ph.D. candidate in Computer Science and Engineering at the University of Michigan where I am advised by Prof. Ron Dreslinski.
Priorly, I received my bachelor’s degree from Addis Ababa Institute of Technology.
Research
My research spans the area of computer architecture, particularly focusing on accelerating data-intensive applications. I design hardware and software optimizations to improve the performance and energy efficiency of data-intensive workloads on conventional and emerging architectures like Processing-in-Memory (PIM).
One of the challenges in the era of big data, is the increasing gap between compute performance and memory bandwidth, which has been partially addressed by in/near memory computing architectures. However, harnessing the high memory bandwidth in these architectures is hampered by the interconnect, which incurs costly data movement. To address this, my thesis proposes custom hardware (memory subsystem including interconnect) and software optimizations to improve the performance and energy efficiency of massively parallel processors.
In my recent work [PACT'22], I have proposed fine-grained inter-GPU data movement and novel caching techniques to improve the performance and scalability of multi-GPU workloads. I have also explored ways to reduce excess data movement in PIM-based graph execution through a processing-in-network solution [ISLPED'19] and multicasting techniques [DATE'20].
Publications

[1] Leul Belayneh, Haojie Ye, Kuan-Yu Chen, David Blaauw, Trevor Mudge, Ronald Dreslinski, Nishil Talati. Locality-aware Optimizations for Improving Remote Memory Latency in Multi-GPU Systems. International Conference on Parallel Architectures and Compilation Techniques (PACT), Chicago, USA, 2022. [PDF]

[2] Nishil Talati, Haojie Ye, Yichen Yang, Leul Belayneh, Kuan-Yu Chen, David Blaauw, Trevor Mudge, Ronald Dreslinski. NDMiner: Accelerating Graph Pattern Mining Using Near Data Processing. International Symposium on Computer Architecture (ISCA), New York, USA, 2022. [PDF]

[3] Leul Belayneh, Valeria Bertacco. GraphVine: Exploiting Multicast for Scalable Graph Analytics. Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France, 2020. [PDF]

[4] Leul Belayneh, Abraham Addisie, Valeria Bertacco. MessageFusion: On-path Message Coalescing for Energy Efficient and Scalable Graph Analytics. International Symposium on Low Power Electronics and Design (ISLPED), Lausanne, Switzerland, 2019. [PDF]

[5] Leul Belayneh, Fitsum Assamnew Andargie, Valeria Bertacco. Archipelago: Architectural Support for Graph Analytics on GPUs. ACM-SRC at International Conference on Parallel Architectures and Compilation Techniques (PACT), 2020. [PDF]
Education
Ph.D. Computer Science and Engineering
University of Michigan, Ann Arbor, MI
Sept 2018 - Present
B.Sc. Electrical and Computer Engineering
Addis Ababa University, Addis Ababa, Ethiopia
Sept 2012 - May 2017
Projects
Contact
2260 Hayward St, MI 48109, USA
leulb@umich.edu