Energy Efficient Task Graph Execution Using Compute Unit Masking in GPUs

Abstract

The frontiers of Supercomputers are pushed by novel discrete accelerators. Accelerators such as GPUs are employed to enable faster execution of Machine Learning, Scientific and High-Performance Computing applications. However, it has been harder to gain increased parallelism in traditional workloads. This is why more focus has been into Task Graphs. AMD’s Directed Acyclic Graph Execution Engine (DAGEE) allows the programmer to define a workload in fine-grained tasks, and the system handles the dependencies at the lower-level. We evaluate DAGEE with the Winograd-Strassen Matrix Multiplication algorithm and show that DAGEE achieves on average 15.3% speed up over the traditional matrix multiplication algorithm.While using DAGEE this may increase the contention among kernels due to the increased amount of parallelism. However, AMD allows the programmer to set the number of active Compute Unit (CU) by masking. This fine-grain scaling allows the system software to enable only the required number of Computation Units within a GPU. Using this mechanism we develop a Runtime that masks CU’s for each task during a task graph execution and partitions each task into their separate CU’s, reducing overall contention and energy consumption. We show that our CU Masking runtime on average reduces energy by 18%.