ELC

Our research focus is on correctness and performance of software on modern computer architectures. Recent advances in multicore and memory technologies provide numerous possibilities to increase the efficiency and performance of software. But the increasing complexity and hardware abstraction provided by today’s programming languages complicate the orchestration of software on the multicore substrate, in cloud-based data centers, and on the blockchain. Current research topics include streaming system orchestration in the Cloud, parallelization of matrix languages and Kronecker algebra operations on heterogeneous multicores and the Cloud, high-level language memory consistency models, graph-neural networks, and safe and efficient smart contract execution on blockchain virtual machines. The most up-to-date overview of our work is provided in our list of publications. Through this project we are investigating orchestration techniques to mitigate performance bottlenecks of cloud-based streaming frameworks and substantially increase their performance. Workload characterization and performance profiling are part of this investigation. It is a challenging problem for developers to fully exploit the performance potential of heterogeneous platforms. CPUs and GPUs have different architectural/performance characteristics, and this difference significantly extends the design-space for partitioning the workload across cores. We have employed our code partitioning technique to accelerate JPEG decoding, and for the parallelization of Kronecker algebra operations. Matrix languages such as Matlab and Octave found widespread use in science and engineering, due to their easy-to-use interactive programming environments. Current implementations do not fully utilize high-performance multicor