Research Overview

Innovations and new technologies are changing our daily lives. We use technology at every moment of our lives, and as a result, we are surrounded by computers in one or the other form. Every computer has a central processing unit to process data. We are interested in the architecture of these processing units, often containing multi-/many-cores, to get the work done faster. We are developing techniques to improve the performance and energy efficiency of multi-/many-core processors such as graphics processing units (GPU). In other words, we are happy when we get more work done from these computing devices with less energy. We hope that our research will reduce our carbon footprint and contribute to attaining the goal of reducing (zero) carbon emissions.

Here is a small example of the potential impact of what we are doing. In 2017, approximately 357 million discrete GPUs were sold in the world. These GPUs consume about 89 million kW energy per hour, assuming 250 W power consumption per GPU, which is the typical power consumption of a discrete GPU. This costs us around 27 million euros per hour. Assume, we can increase the energy efficiency of a GPU by a mere 5%. When we scale over the 357 million discrete GPUs, it can save us 4.5 million kW energy per hour and 1.35 million euros per hour. If we put it into a different perspective, this much saving is enough to provide electricity to 11.3 million households in Germany for one hour. In my opinion, this is a lot! Dr. Lal's Ph.D. research improved GPU‘s energy efficiency by about 20%. Although, the research so far has been restricted to the research community and not implemented in a real GPU. This is one of the areas that we are looking to take forward.

If you are interested in doing research with our group, you may send an e-mail. If you are already at Hamburg University of Technology, you can also schedule an in-person meeting.

Broad Areas of Research

  • Energy-Efficient GPU architecture
  • Applying machine learning for microarchitectural improvements
  • Power and performance modeling
  • Runtime support for HPC clusters
  • Memory system, MAG-aware memory compression
  • Heterogeneous computing (porting/optimizing applications)
  • Approximate computing
  • GPU security

Past Projects