DocumentCode :
668175
Title :
Oncilla: A GAS runtime for efficient resource allocation and data movement in accelerated clusters
Author :
Young, James ; Se Hoon Shon ; Yalamanchili, Sudhakar ; Merritt, Alex ; Schwan, Karsten ; Froning, Holger
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2013
fDate :
23-27 Sept. 2013
Firstpage :
1
Lastpage :
8
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.
Keywords :
application program interfaces; cloud computing; data warehouses; graphics processing units; parallel processing; processor scheduling; resource allocation; storage management; tree searching; Big Data applications; GAS runtime; Oncilla API; TPC-H data warehousing benchmark; breadth-first search benchmark; cloud computing; cluster acceleration; cluster resource allocation; cluster resource management; data movement; data transfer; global address spaces; graphics processing units; heterogeneous CPU cluster scheduling; heterogeneous GPU cluster scheduling; high-performance computing; memory regions; Computational modeling; Graphics processing units; Performance evaluation; Random access memory; Resource management; Runtime; Schedules;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cluster Computing (CLUSTER), 2013 IEEE International Conference on
Conference_Location :
Indianapolis, IN
Type :
conf
DOI :
10.1109/CLUSTER.2013.6702679
Filename :
6702679
Link To Document :
بازگشت