Tom Scogland

Pratheek Prakash Shetty, Thomas R. W. Scogland, Wu-chun Feng

Concurrent queues can significantly impact supercomputing performance by being critical bottlenecks for task distribution, load balancing, and resource utilization. As HPC systems move beyond 10-million processor cores, the ability to rapidly move items between producer and consumer threads without excessive locking is essential for efficient queues, preventing idle cores, maximizing utilization, and achieving high parallel speedup. While concurrent queues are well studied on CPUs, they remain largely unexplored on modern GPUs, where SIMT execution, massive parallelism, and atomic contention reshape the design space. We present three linearizable GPU concurrent queues spanning from lock-free to wait-free guarantees: (1) G-WFQ-YMC, an adaptation of Yang and Mellor-Crummey's wait-free queue using preallocated segments; (2) G-LFQ, a bounded lock-free queue that uses wave-batched fast paths to maximize throughput; and (3) G-WFQ, a bounded wait-free queue that packs shared state into 64-bit compare-and-swap operations while preserving linearizability and bounded memory.

Loïc Pottier, Konstantia Georgouli, Timothy S. Carpenter et al.

Computational models have become one of the prevalent methods to model complex phenomena. To accurately model complex interactions, such as detailed biomolecular interactions, scientists often rely on multiscale models comprised of several internal models operating at difference scales, ranging from microscopic to macroscopic length and time scales. Bridging the gap between different time and length scales has historically been challenging but the advent of newer machine learning (ML) approaches has shown promise for tackling that task. Multiscale models require massive amounts of computational power and a powerful workflow management system. Orchestrating ML-driven multiscale studies on parallel systems with thousands of nodes is challenging, the workflow must schedule, allocate and control thousands of simulations operating at different scales. Here, we discuss the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a multiscale workflow management infrastructure, that can orchestrate thousands of molecular dynamics (MD) simulations operating at different timescales, spanning from millisecond to nanosecond. More specifically, we introduce a novel version of MuMMI called "mini-MuMMI". Mini-MuMMI is a curated version of MuMMI designed to run on modest HPC systems or even laptops whereas MuMMI requires larger HPC systems. We demonstrate mini-MuMMI utility by exploring RAS-RAF membrane interactions and discuss the different challenges behind the generalization of multiscale workflows and how mini-MuMMI can be leveraged to target a broader range of applications outside of MD and RAS-RAF interactions.

Harshitha Menon, Konstantinos Parasyris, Tom Scogland et al.

Modern software is incredibly complex. A typical application may comprise hundreds or thousands of reusable components. Automated package managers can help to maintain a consistent set of dependency versions, but ultimately the solvers in these systems rely on constraints generated by humans. At scale, small errors add up, and it becomes increasingly difficult to find high-fidelity configurations. We cannot test all configurations, because the space is combinatorial, so exhaustive exploration is infeasible. In this paper, we present Reliabuild, an auto-tuning framework that efficiently explores the build configuration space and learns which package versions are likely to result in a successful configuration. We implement two models in Reliabuild to rank the different configurations and use adaptive sampling to select good configurations with fewer samples. We demonstrate Reliabuild's effectiveness by evaluating 31,186 build configurations of 61 packages from the Extreme-scale Scientific Software Stack(E4S). Reliabuild selects good configurations efficiently. For example, Reliabuild selects 3X the number of good configurations in comparison to random sampling for several packages including Abyss, Bolt, libnrm, OpenMPI. Our framework is also able to select all the high-fidelity builds in half the number of samples required by random sampling for packages such as Chai, OpenMPI, py-petsc4py, and slepc. We further use the model to learn statistics about the compatibility of different packages, which will enable package solvers to better select high-fidelity build configurations automatically.