Short Bio

Sohan Lal is a junior professor and head of the Institue of Massively Parallel Systems at Hamburg University of Technology (TUHH). Before joining TUHH, he was a postdoctoral researcher at the Technical University of Berlin (TU Berlin), working on a DFG funded research project on advanced modeling and runtime support for large-scale HPC clusters (Celerity). He graduated with a Ph.D. in Computer Science from TU Berlin in August 2019. His dissertation was titled “Power Modeling and Architectural Techniques for Energy-Efficient GPUs” and was supervised by Prof. Ben Juurlink. At TU Berlin, he worked on two EU funded research projects on low power parallel computing on GPUs (LPGPU), where he led several tasks, collaborated with consortium members to deliver joint deliverables and contributed significantly to their success. His Ph.D. dissertation work was also conducted in the context of LPGPU projects. For his dissertation, he investigated bottlenecks that cause low performance and low energy efficiency in GPUs and proposed architectural techniques to improve performance and energy efficiency. The results of the dissertation were published in several reputed conferences such as IPDPS, DATE, ISPASS. He won several grants such as HiPEAC travel grants, a HiPEAC collaboration grant to visit Prof. Henk Coporaal (TU/e) that led to a joint publication at DATE. He was a semifinalist at ACM SRC held at MICRO'18. He is interested in computer architecture in general and graphics processing unit (GPU) architecture in particular and his broad research interests include power and performance modeling, parallel systems, memory systems, heterogeneous computing, approximate computing, applied machine learning, and GPU security.

Before Ph.D., he received his masters from the Indian Institute of Technology Delhi (IITD) in 2011. Before that, he worked as a Lecturer at Shri Mata Vaishno Devi University (SMVDU), which was the first teaching stint that made him deeply passionate and excited about teaching and mentorship. He also worked as an IT specialist in the Government of India. He received his bachelor in Computer Science and Engineering from Government College of Engineering and Technology (GCET), Jammu, India in 2003.


  • 🎓 Ph.D. (Dr.-Ing.) in Computer Science, 2019, Technical University of Berlin
  • 🎓 M.S (Research) in Information Technology, 2011, Indian Institute of Technology Delhi
  • 🎓 B.Tech in Computer Science & Engineering, 2003, Govt. College of Engineering & Technology Jammu

Invited Talks

On Performance and Energy Efficiency of GPUs

  • Technical University of Hamburg, November 2020
  • Queens University of Belfast, January 2020

A Perspective on Performance, Power, and Energy Efficiency of GPUs

  • Shri Govindram Seksaria Institute of Technology and Science, SGSITS, Indore, June 2020

Architectural Techniques for Energy-Efficient GPUs

  • RWTH Aachen, April 2019
  • Indian Institute of Technology Jammu, March 2019
  • Indian Institute of Technology Ropar, March 2019
  • Technical University of Dresden, February 2019

Conference Talks

  • QSLC: Quantization-Based, Low-Error Selective Approximation for GPUs (DATE’21)
  • SYCL-Bench: A Versatile Cross-Platform Benchmark Suite for Heterogeneous Computing (Euro-Par’20)
  • A Quantitative Study of Locality in GPU Caches (SAMOS’20)
  • Memory Access Granularity Aware Selective Lossy Compression for GPUs (DATE’19)
  • A Case for Memory Access Granularity Aware Selective Lossy Compression for GPUs (MICRO’18)
  • E2MC: Entropy Encoding Based Memory Compression for GPUs (IPDPS’17)
  • GPGPU Workload Characteristics and Performance Analysis (SAMOS’14)
  • Exploring GPGPU Workload Characteristics and Power Consumption (ACACES’13)
  • A Framework for Modeling GPU Power Consumption (HiPEAC’13)