Distributing Simplex-Shaped Nested for-Loops to Identify Carcinogenic Gene Combinations

Abstract

Cancer is a leading cause of death in the US, and it results from a combination of two-nine genetic mutations. Identifying five-hit combinations responsible for several cancer types is computationally intractable even with the fastest super-computers in the USA. Iterating through nested loops required by the process presents a simplex-shaped workload with irregular memory access patterns. Distributing this workload efficiently across thousands of GPUs offers a challenge in dividing simplex-shaped (triangular/tetrahedral) workload into similar shapes with equal volume. Irregular memory access patterns create imbalanced compute utilization across nodes. We developed a generalized solution for distributing a simplex-shaped workload by partially coalescing the nested for-loops, minimizing the memory access overhead by efficiently utilizing limited shared memory, a dynamic scheduler, and loop tiling. For 4-hit combinations, we achieved a 90% − 100% strong scaling efficiency for up to 3594 V100 GPUs on the Summit supercomputer. Finally, we designed and implemented a distributed algorithm to identify 5-hit combinations for four different cancer types, and the identified combinations can differentiate between cancer and normal samples with 86.59−88.79% precision and 84.42 − 90.91% recall. We also demonstrated the robustness of our solution by porting the code to another leadership class computing platform Crusher, a testbed for the fastest supercomputer Frontier. On Crusher, we achieved 98% strong scaling efficiency on 50 nodes (400 AMD MI250X GCDs) and demonstrated the computational readiness of Frontier for scientific applications.