Abstract: Large scale, data-intensive applications pose challenges to systems with a traditional memory hierarchy due to their unstructured data sources and irregular memory access patterns. In modern supercomputing applications, communication dominates computation. In the analytics world, when the data size of an application becomes sufficiently large, there is a problem keeping processors busy, which in turn leads to the need for faster memory and bandwidth. The rate of improvement in microprocessor speed greatly exceeds the rate of improvement in DRAM memory speed.
In order to overcome this limitation of bandwidth speed, inconsistent with processor speed, asynchronous programming provides a way to deal with blocking waits and executes events independent of the main program flow. We address the data movement and memory bandwidth problems occurring in hybrid computer architectures by effectively utilizing the intra-node parallelism and inter-node parallelism using the concurrency provided by the underlying processor systems. This software-hardware co-design is the technical equivalent of solving a multiple constraint optimization problem.
In this seminar, I will be going over the works on Coherent Accelerator Processor Interface (OpenCAPI), EMU Parallel Migratory Thread Architecture, Persistent Memory, Graph Algorithms and interactive real time data science.
Please use this link to attend the virtual seminar.
Meeting ID: 722029563 / Participant passcode: 8406