Work

IVM: a task-based shared memory programming model and runtime system to enable uniform access to CPU-GPU clusters

Year: 2016

Type: conference-paper

Abstract: GPUs have been widely used to accelerate a variety of applications from different domains and have become part of high-performance computing clusters. Yet, the use of GPUs within distributed applications still faces significant challenges in terms of programmability and performance portability. The use of popular programming models for distributed applications (such as MPI, SHMEM, and Charm++) in combination with GPU programming frameworks (such as CUDA and OpenCL) exposes to the programmer disjoint memory address spaces and provides a non-uniform view of compute resources (i.e., CPUs and GPUs). In addition, these programming models often perform static assignment of tasks to compute resources and require significant programming effort to embed dynamic scheduling and load balancing mechanisms within the application.

Source: ACM International Conference on Computing Frontiers (CF)

DOI: 10.1145/2903150.2903174

Authors: Kittisak Sajjapongse, Ruidong Gu, Michela Becchi

Institution: University of Missouri

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, Nvidia

Awards: MRI: Acquisition of Instrument for Data-intensive Applications with Hybrid Cloud Computing Needs, CSR: Small: Scheduling and Virtualization Technologies for Heterogeneous Clusters with Many-core Devices