Pregled bibliografske jedinice broj: 1207084
Towards intelligent compiler optimization
Towards intelligent compiler optimization // 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO)
Opatija, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2022. str. 948-953 doi:10.23919/mipro55190.2022.9803630 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1207084 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Towards intelligent compiler optimization
Autori
Kovac, Mihael ; Brcic, Mario ; Krajna, Agneza ; Krleza, Dalibor
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO)
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2022, 948-953
Skup
45th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2022)
Mjesto i datum
Opatija, Hrvatska, 23.05.2022. - 27.05.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
compiler optimization , GNN , reinforcement learning , edge computing , heterogeneous computing , polyhedral model , machine learning
Sažetak
The future of computation is massively parallel and heterogeneous with specialized accelerator devices and instruction sets in both edge- and cluster-computing. However, software development is bound to become the bottleneck. To extract the potential of hardware wonders, the software would have to solve the following problems: heterogeneous device mapping, capability discovery, parallelization, adaptation to new ISAs, and many others. This systematic complexity will be impossible to manually tame for human developers. These problems need to be offloaded to intelligent compilers. In this paper, we present the current research that utilizes deep learning, polyhedral optimization, reinforcement learning, etc. We envision the future of compilers as consisting of empirical testing, automatic statistics collection, continual learning, device capability discovery, multiphase compiling – precompiling and JIT tuning, and classification of workloads. We devise a simple classification experiment to demonstrate the power of simple graph neural networks (GNNs) paired with program graphs. The test performance demonstrates the effectiveness and representational appropriateness of GNNs for compiler optimizations in heterogeneous systems. The benefits of intelligent compilers are time savings for the economy, energy savings for the environment, and greater democratization of software development.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Računarstvo, Interdisciplinarne tehničke znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb