The minos computing library: Efficient parallel programming for extremely heterogeneous systems
Proceedings of the 13th Annual Workshop on General Purpose Processing Using …, 2020•dl.acm.org
Hardware specialization has become the silver bullet to achieve efficient high performance,
from Systems-on-Chip systems, where hardware specialization can be" extreme", to large-
scale HPC systems. As the complexity of the systems increases, so does the complexity of
programming such architectures in a portable way. This work introduces the Minos
Computing Library (MCL), as system software, programming model, and programming
model runtime that facilitate programming extremely heterogeneous systems. MCL supports …
from Systems-on-Chip systems, where hardware specialization can be" extreme", to large-
scale HPC systems. As the complexity of the systems increases, so does the complexity of
programming such architectures in a portable way. This work introduces the Minos
Computing Library (MCL), as system software, programming model, and programming
model runtime that facilitate programming extremely heterogeneous systems. MCL supports …
Hardware specialization has become the silver bullet to achieve efficient high performance, from Systems-on-Chip systems, where hardware specialization can be "extreme", to large-scale HPC systems. As the complexity of the systems increases, so does the complexity of programming such architectures in a portable way.
This work introduces the Minos Computing Library (MCL), as system software, programming model, and programming model runtime that facilitate programming extremely heterogeneous systems. MCL supports the execution of several multi-threaded applications within the same compute node, performs asynchronous execution of application tasks, efficiently balances computation across hardware resources, and provides performance portability.
We show that code developed on a personal desktop automatically scales up to fully utilize powerful workstations with 8 GPUs and down to power-efficient embedded systems. MCL provides up to 17.5x speedup over OpenCL on NVIDIA DGX-1 systems and up to 1.88x speedup on single-GPU systems. In multi-application workloads, MCL's dynamic resource allocation provides up to 2.43x performance improvement over manual, static resources allocation.
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