I am a Ph.D. candidate in Computer Science and Engineering at the University of Michigan, Ann Arbor where I am advised by Professor Valeria Bertacco. I am member of the Computer Engineering Lab (CE Lab) and affiliated with the Applications Driving Architectures (ADA) center. I received my bachelor’s degree from Addis Ababa Institute of Technology, Addis Ababa.
I was born in Addis Ababa, Ethiopia and raised in Hawassa, a small town in southern Ethiopia. You can find more about me here.
My research spans the area of computer architecture, particularly focusing on the acceleration of data-centered applications. I am currently working on addressing the inefficient execution of irregular workloads on GPUs via a custom memory subsystem. Priorly, I have been working on alleviating interconnect traffic bottlenecks in processing-in-memory based graph execution [ISLPED' 19, DATE'20].
 Leul Belayneh, Valeria Bertacco. GraphVine: Exploiting Multicast for Scalable Graph Analytics. Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France, 2020. [PDF]
 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]
 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]
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
In-depth Characterization and Architectural Support for Irregular Workloads on GPUs
In this work, sources of inefficiencies in GPUs are identified, analyzed, and addressed via combined software-hardware optimizations. Specifically, I designed and implemented architectural enhancements to the memory subsystem of GPUs so as to efficiently utilize their enormous memory bandwidth and computing power.
Exploiting Power-Law for Graph Prefetching
Irregular workloads, particularly graph analytics, benefits less from conventional prefetching mechanisms. In this work, we applied software-based prefetching that targets vertices with significant outgoing edges in power-law graphs (i.e. top 20%). LLVM-based selective insertion of prefetches minimizes unwanted prefetching,thus alleviating cache pollution.
Multicast for Scalable Graph Analytics
In most graph workloads, a source vertex sends out similar vertex-update messages to its neighboring vertices. GraphVine [DATE’20] exploits multicasting to combine similar messages into a multicast packet which alleviates network traffic.
Processing in Network Solution for Scalable Graph Analytics
Commutative and associative reduction operations in graph analytics, allows distributed computation via compute-capable routers. Hence, MessageFusion [ISLPED’19] coalesces vertex-update messages traversing to a same destination so as to reduce overall network traffic.
Software-based teaching aid for Signal Processing and Digital Communication
Delivered an easy-to-use software tool for education support in IoT at Addis Ababa Institute of Technology.
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