Allok: a machine learning approach for efficient graph execution on CPU–GPU clusters

The unprecedented increase in interconnected data has driven the development of efficient graph analytics for extensive data analysis, resulting in improvements across various domains. Prior work has focused on optimizing graph execution for both CPUs and GPUs while overlooking the scalability of graph applications and the selection of an ideal architecture. Thus, we propose Allok, a flexible graph processing framework that aids in selecting the optimal processing architecture (CPU or GPU) for a batch of graph applications while also optimizing number of threads on CPUs. Allok relies solely on high-level graph features to make decisions without the need for further application execution. Our experiments on an HPC system with 4 CPUs and 3 GPUs running 5 algorithms over 25 input graphs show that Allok reduces application execution time by an average of 67.26 \(\times\) $\times$ and reduces energy consumption and energy-delay product by an average of 18.06 \(\times\) $\times$ and 1237.96 \(\times\) $\times$ , respectively.