Cross Resource Optimisation of Database Functionality across Heterogeneous Processors

Significant application performance improvements can be achieved by heterogeneous compute technologies, such as multi-core CPUs, GPUs and FPGAs. The HARNESS project is developing architectural principles that enable the next generation cloud platforms to incorporate such devices thereby vastly incre...

Full description

Saved in:
Bibliographic Details
Main Authors: O'Neill, Eoghan, McGlone, John, Coutinho, J.G.F, Doole, Andrew, Ragusa, Carmelo, Pell, O., Sanders, P.
Format: Conference Proceeding
Language:English
Subjects:
Dictionaries
Field programmable gate arrays
heterogeneous computation
in-memory database
Kernel
Performance evaluation
Resource management
run-time allocation
Runtime
SAP HANA
shepard
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Significant application performance improvements can be achieved by heterogeneous compute technologies, such as multi-core CPUs, GPUs and FPGAs. The HARNESS project is developing architectural principles that enable the next generation cloud platforms to incorporate such devices thereby vastly increasing performance, reducing energy consumption, and lowering associated cost profiles. Along with management and integration of such devices in a cloud environment, a key issue is enabling enterprise-level software to make effective use of such compute devices. A major obstacle in adopting heterogeneous compute resources is the requirement that at design time the developer must decide on which device to execute portions of the application. For an interactive application, such as SAP HANA where there are many on-going tasks and processes, this type of decision is impossible to predict at design time. What is required is the ability to decide, at run-time, the optimal compute device to execute a task. This paper extends upon existing work on SHEPARD to support non-OpenCL devices. SHEPARD decouples application development from the target platform and enables the required run-time allocation of tasks to heterogeneous computing devices. This paper establishes SHEPARD's capability to: (1) select the appropriate compute device to execute tasks, (2) dynamically load the device application code at runtime, and (3) execute the application logic. Experiments demonstrate how SHEPARD optimises the execution of a SAP HANA database management function across heterogeneous compute devices and perform automatic run-time task allocation.
ISSN:2158-9178
DOI:10.1109/ISPA.2014.28