The Community for Technology Leaders
2013 IEEE International Conference on Cluster Computing (CLUSTER) (2013)
Indianapolis, IN, USA
Sept. 23, 2013 to Sept. 27, 2013
ISBN: 978-1-4799-0898-1
pp: 1-8
Jeff Young , School of Electrical and Computer Engineering, Georgia Institute of Technology, USA
Se Hoon Shon , School of Electrical and Computer Engineering, Georgia Institute of Technology, USA
Sudhakar Yalamanchili , School of Electrical and Computer Engineering, Georgia Institute of Technology, USA
Alex Merritt , College of Computing, Georgia Institute of Technology, USA
Karsten Schwan , College of Computing, Georgia Institute of Technology, USA
Holger Froning , Institute of Computer Engineering, University of Heidelberg, Germany
ABSTRACT
Accelerated and in-core implementations of Big Data applications typically require large amounts of host and accelerator memory as well as efficient mechanisms for transferring data to and from accelerators in heterogeneous clusters. Scheduling for heterogeneous CPU and GPU clusters has been investigated in depth in the high-performance computing (HPC) and cloud computing arenas, but there has been less emphasis on the management of cluster resource that is required to schedule applications across multiple nodes and devices. Previous approaches to address this resource management problem have focused on either using low-performance software layers or on adapting complex data movement techniques from the HPC arena, which reduces performance and creates barriers for migrating applications to new heterogeneous cluster architectures. This work proposes a new system architecture for cluster resource allocation and data movement built around the concept of managed Global Address Spaces (GAS), or dynamically aggregated memory regions that span multiple nodes.We propose a software layer called Oncilla that uses a simple runtime and API to take advantage of non-coherent hardware support for GAS. The Oncilla runtime is evaluated using two different high-performance networks for microkernels representative of the TPC-H data warehousing benchmark, and this runtime enables a reduction in runtime of up to 81%, on average, when compared with standard disk-based data storage techniques. The use of the Oncilla API is also evaluated for a simple breadth-first search (BFS) benchmark to demonstrate how existing applications can incorporate support for managed GAS.
INDEX TERMS
CITATION

J. Young, S. H. Shon, S. Yalamanchili, A. Merritt, K. Schwan and H. Froning, "Oncilla: A GAS runtime for efficient resource allocation and data movement in accelerated clusters," 2013 IEEE International Conference on Cluster Computing (CLUSTER), Indianapolis, IN, USA USA, 2014, pp. 1-8.
doi:10.1109/CLUSTER.2013.6702679
91 ms
(Ver 3.3 (11022016))