Welcome at the Institute of Massively Parallel Systems!

The Institute of Massively Parallel Systems (MPS) investigates and teaches in the field of computer architecture, focusing on massively parallel systems. These days massively parallel systems are present everywhere – in our smartphones, cars, supercomputers. They help us to do many things which were not possible before. For example, the recent stupendous success of machine learning, especially deep learning, is mainly due to the exponential increase in computational power. As such, machine learning is not new. Machine learning methods are around since the 1950s. What has predominately changed now is the computing power, with many-core processors such as GPUs as the main drivers. If these massively parallel processors are not utilized properly, they are very expensive in terms of power and energy consumption, which is not good as we aspire to reduce our carbon footprint. At MPS, we are working to make computing devices more performance and energy-efficient from the architecture perspective, and improve their programmability from a programmer’s perspective.

News

2022-11-16: MPS is growing!

Mr. Tim Lühnen joined the MPS group as a research assistant/Ph.D. student. Welcome, Tim! Tim will be strengthing the teaching and research of the MPS group. Tim is interested in GPU architectures as well as RISC-V processors. We are excited to have him on the team and looking forward to expanding the research portfolio of MPS.

2022-07-01: Manuel Renz joins MPS as a Ph.D. student

Mr. Manuel Renz joined the MPS group as a Ph.D. student. Welcome, Manuel! Manuel is interested in computer architecture and he will be working to improve the architecture of multi-/many-core processors, in particular the memory system. We are excited to explore new research opportunities with him.

2022-02-21: MPS Paper Accepted at IJPP 2022

The paper "A Quantitative Study of Locality in GPU Caches for Memory-Divergent Workloads" by Sohan Lal, Sharatchandra V. Bogaraju, and Ben Juurlink has been accepted for publication at the International Journal of Parallel Programming (IJPP) 2022. Congratulations to the team!

2022-01-22: MPS Paper Accepted at IPDPS 2022

The paper "Memory Access Granularity Aware Lossless Compression for GPUs" by Sohan Lal, Manuel Renz, Julian Hartmer, and Ben Juurlink has been accepted for publication at the International Conference on Parallel and Distributed Systems (IPDPS) 2022. Congratulations to the team!

2021-09-01: Sohan Lal Joins TUHH

Dr. Sohan Lal joins TU Hamburg as a Junior Professor to lead the institute of massively parallel systems.