DataSys: Data-Intensive Distributed Systems LaboratoryData-Intensive Distributed Systems Laboratory

Illinois Institute of Technology
Department of Computer Science

GEMTC: GPU Enabled Many-Task Computing

Current software and hardware limitations prevent Many-Task Computing (MTC) workloads from leveraging hardware accelerators (NVIDIA GPUs, Intel Xeon Phi) boasting Many-Core Computing architectures. Some broad application classes that fit the MTC paradigm are workflows, MapReduce, high-throughput computing, and a subset of high-performance computing. MTC emphasizes using many computing resources over short periods of time to accomplish many computational tasks (i.e. including both dependent and independent tasks), where the primary metrics are measured in seconds. MTC has already proven successful in Grid Computing and Supercomputing on MIMD architectures, but the SIMD architectures of today’s accelerators pose many challenges in the efficient support of MTC workloads on accelerators. This work aims to address the programmability gap between MTC and accelerators, through an innovative middleware that enables MIMD programmability of SIMD architectures. This work will enable a broader class of applications to leverage the growing number of accelerated high-end computing systems.