A heterogeneous system is one that uses more than one kind of processors or cores. It typically uses a general-purpose CPU to run an operating system and accelerators, such as GPUs and FPGAs, to perform some specific tasks faster and energy-efficiently. Heterogeneous systems that are based on GPUs and FPGAs are widening their user base these days. Especially, GPU-based heterogeneous systems are de facto standard for running deep learning applications. In the post-Moore era, the role of accelerator-based heterogeneous systems is becoming more important. Moreover, GPU-based heterogeneous systems are de facto standard for executing Deep Learning applications.
Ideally, software designers would like to extract performance and throughput gains proportional to the increase in the processor resources in the system. Unfortunately, a major challenge, the programming wall, needs to be addressed before such a goal can be achieved. It is an obstacle for general programmers and deep learning model designers to efficiently parallelize and optimize their applications to exploit their processor resources.
The THUNDER Research Group is a research group at Seoul National University and belongs to both the Department of Computer Science and Engineering in the College of Engineering and the Department of Data Science in the Graduate School of Data Science. Its goal is to overcome the programming wall by means of Deep Learning models and compiler, runtime, architecture, and operating system techniques at various levels taking pragmatic approaches. Its most recent research focuses on the following:
Startups by THUNDER Research Group alumni: