GPUs, which are widely used high-performance hardware accelerators in heterogeneous computing, and programming models for architectures such as OpenCL and CUDA, have recently been developed to achieve high productivity. LLVM is an open-source compiler infrastructure that enables low-level optimization through LLVM intermediate representation (LLVM IR) in various programming language environments. In this paper, we propose a fully-automatic Dynamic Profiling framework which performs instruction-level analysis through IR-level code instrumentation for typical OpenCL workload kernels.
|Title of host publication||Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2019 Feb 22|
|Event||2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of|
Duration: 2018 Oct 28 → 2018 Oct 31
|Name||IEEE Region 10 Annual International Conference, Proceedings/TENCON|
|Conference||2018 IEEE Region 10 Conference, TENCON 2018|
|Country/Territory||Korea, Republic of|
|Period||18/10/28 → 18/10/31|
Bibliographical noteFunding Information:
ACKNOWLEDGEMENT This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP)(No. NRF-2015R1C1A1A01053844), ICT R&D program of MSIP/IITP (No. 2017-0-00142), and the R&D program of MOTIE/KEIT (No. 10077609).
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering