Hagedorn, Christopher
[VerfasserIn]
;
Runge, Jakob
[AkademischeR BetreuerIn];
Le, Thuc D.
[AkademischeR BetreuerIn];
Plattner, Hasso
[AkademischeR BetreuerIn]Universität Potsdam
Parallel execution of causal structure learning on graphics processing units
Hochschulschrift:
Dissertation, Universität Potsdam, 2023
Anmerkungen:
Beschreibung:
Learning the causal structures from observational data is an omnipresent challenge in data science. The amount of observational data available to Causal Structure Learning (CSL) algorithms is increasing as data is collected at high frequency from many data sources nowadays. While processing more data generally yields higher accuracy in CSL, the concomitant increase in the runtime of CSL algorithms hinders their widespread adoption in practice. CSL is a parallelizable problem. Existing parallel CSL algorithms address execution on multi-core Central Processing Units (CPUs) with dozens of compute cores. However, modern computing systems are often heterogeneous and equipped with Graphics Processing Units (GPUs) to accelerate computations. Typically, these GPUs provide several thousand compute cores for massively parallel data processing. To shorten the runtime of CSL algorithms, we design efficient execution strategies that leverage the parallel processing power of GPUs. Particularly, we derive GPU-accelerated variants of a well-known ...