Benjamin Eagan

Investigating Performance Benefits from OpenACC Kernel Directives

This report investigates the use of High Performance Computing resources for real-time computer v... more This report investigates the use of High Performance Computing resources for real-time computer vision applications through two test cases. The first test case explores parallel hand tracking for use in Human Computer Interaction applications using a Shared Memory pipeline. Results confirmed improved capabilities through the use of high-definition video sources. The second test case investigates if Graph-Cut stereo matching can be performed in real-time using High Performance Computing to produce a disparity map of an environment. Accurate real-time disparity maps were not produced in this report, however results suggest it is both possible and promising.

Download

Investigating Performance Benefits from OpenACC Kernel Directives

Parallel Computing: Accelerating Computational Science and Engineering (CSE), Mar 1, 2014

OpenACC is a high-level programming model that uses directives for offloading computation to acce... more OpenACC is a high-level programming model that uses directives for offloading computation to accelerators. This paper explores the benefit of using OpenACC performance tuning directives to manually specify GPU scheduling, versus the scheduling OpenACC applies by default. We performed manual scheduling using gang and vector clauses in a directive, and applied to matrix-matrix multiply and Classical Gram-Schmidt orthonormalisation test cases. We then tested using the NVIDIA M2090 and K20 GPGPUs, in conjunction with both the PGI and CAPS implementations of OpenACC. The speedup realised by tuning the gang and vector values ranged from 1.0 to 3.1 in the test cases examined. This shows that the gang and vector values have a large impact on performance, and in some cases the compilers are able to automatically select ideal gang and vector values.

Uploads

Papers by Benjamin Eagan

Log In