arXiv subjects

Robert Wisniewski

Publications and source records attributed to Robert Wisniewski.

2 recordsLinked to original sources

arXiv2025

EDAN: Towards Understanding Memory Parallelism and Latency Sensitivity in HPC

Resource disaggregation is a promising technique for improving the efficiency of large-scale computing systems. However, this comes at the cost of increased memory access latency due to the need to rely on the network fabric to transfer data between remote nodes. As such, it is crucial to ascertain an application's memory latency sensitivity to minimize the overall performance impact. Existing tools for measuring memory latency sensitivity often rely on custom ad-hoc hardware or cycle-accurate simulators, which can be inflexible and time-consuming. To address this, we present EDAN (Execution DAG Analyzer), a novel performance analysis tool that leverages an application's runtime instruction trace to generate its corresponding execution DAG. This approach allows us to estimate the latency sensitivity of sequential programs and investigate the impact of different hardware configurations. EDAN not only provides us with the capability of calculating the theoretical bounds for performance metrics, but it also helps us gain insight into the memory-level parallelism inherent to HPC applications. We apply EDAN to applications and benchmarks such as PolyBench, HPCG, and LULESH to unveil the characteristics of their intrinsic memory-level parallelism and latency sensitivity.

cs.PF↗

arXiv2024

LLAMP: Assessing Network Latency Tolerance of HPC Applications with Linear Programming

The shift towards high-bandwidth networks driven by AI workloads in data centers and HPC clusters has unintentionally aggravated network latency, adversely affecting the performance of communication-intensive HPC applications. As large-scale MPI applications often exhibit significant differences in their network latency tolerance, it is crucial to accurately determine the extent of network latency an application can withstand without significant performance degradation. Current approaches to assessing this metric often rely on specialized hardware or network simulators, which can be inflexible and time-consuming. In response, we introduce LLAMP, a novel toolchain that offers an efficient, analytical approach to evaluating HPC applications' network latency tolerance using the LogGPS model and linear programming. LLAMP equips software developers and network architects with essential insights for optimizing HPC infrastructures and strategically deploying applications to minimize latency impacts. Through our validation on a variety of MPI applications like MILC, LULESH, and LAMMPS, we demonstrate our tool's high accuracy, with relative prediction errors generally below 2%. Additionally, we include a case study of the ICON weather and climate model to illustrate LLAMP's broad applicability in evaluating collective algorithms and network topologies.

cs.DC↗