Paving the Road to Exascale with Many-Task Computing

Exascale systems will bring significant challenges. This work attempts to addresses them through the Many-Task Computing (MTC) paradigm, by delivering data-aware job scheduling systems and fully asynchronous distributed architectures. MTC applications are structured as DAG graphs of tasks, with dependencies forming the edges. The asynchronous nature of MTC makes it more resilient than traditional HPC approaches as the system MTTF decreases. Future highly parallelized hardware is well suited for achieving high throughput with large-scale MTC applications. This work proposes a distributed MTC execution fabric for exascale, MATRIX, which adopts work stealing to achieve load balance. Work stealing was studied through SimMatrix, a scalable simulator, up to exascale with millions of nodes, billions of cores, and trillions of tasks.