38 citations · 87 across the 7 of their papers we have counts for
4 papers · 1 filter
Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach
Urvij Saroliya, Eishi Arima, Dai Liu +1
GPU-based heterogeneous architectures are now commonly used in HPC clusters. Due to their architectural simplicity specialized for data-level parallelism, GPUs can offer much highe…
On the Convergence of Malleability and the HPC PowerStack: Exploiting Dynamism in Over-Provisioned and Power-Constrained HPC Systems
Eishi Arima, Isaías A. Comprés, Martin Schulz
Recent High-Performance Computing (HPC) systems are facing important challenges, such as massive power consumption, while at the same time significantly under-utilized system resou…
Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps
Eishi Arima, Minjoon Kang, Issa Saba +3
CPU-GPU heterogeneous systems are now commonly used in HPC (High-Performance Computing). However, improving the utilization and energy-efficiency of such systems is still one of th…
Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning
Issa Saba, Eishi Arima, Dai Liu +1
CPU-GPU heterogeneous architectures are now commonly used in a wide variety of computing systems from mobile devices to supercomputers. Maximizing the throughput for multi-programm…