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The supercomputing platforms available for high performance computing based research evolve at a great rate. However, this rapid development of novel technologies requires constant adaptations and optimizations of the existing codes for…
Using a realistic molecular catalyst system, we conduct scaling studies of ab initio molecular dynamics simulations using the CP2K code on both Intel Xeon CPU and NVIDIA V100 GPU architectures. We explore using process placement and…
Understanding the behavior of simulated architectures in gem5 is critical for studying complex, deeply integrated computing systems. However, conventional analysis methods provide only an indirect view of the simulated system internals. In…
Architectural simulators play a critical role in early microarchitectural exploration due to their flexibility and high productivity. However, their effectiveness is often constrained by fidelity: simulators may deviate from the behavior of…
We carry out a comparative performance study of multi-core CPUs, GPUs and Intel Xeon Phi (Many Integrated Core - MIC) with a microscopy image analysis application. We experimentally evaluate the performance of computing devices on core…
The unknown parameters of simulation models often need to be calibrated using observed data. When simulation models are expensive, calibration is usually carried out with an emulator. The effectiveness of the calibration process can be…
As processors increase in complexity, costs grow even more rapidly, both for functional verification and performance validation. Most often, silicon characterizations comprise simple performance counters, which are aggregated and separated…
In the push for exascale computing, energy efficiency is of utmost concern. System architectures often adopt accelerators to hasten application execution at the cost of power. The Intel Xeon Phi co-processor is unique accelerator that…
Parallel computing is a standard approach to achieving high-performance computing (HPC). Three commonly used methods to implement parallel computing include: 1) applying multithreading technology on single-core or multi-core CPUs; 2)…
Graphics processing units (GPUs) are now considered the leading hardware to accelerate general-purpose workloads such as AI, data analytics, and HPC. Over the last decade, researchers have focused on demystifying and evaluating the…
Chiplet architectures are on the rise as they promise to overcome the scaling challenges of monolithic chips. A key component of such architectures is an efficient inter-chiplet interconnect (ICI). The ICI design space is huge as there are…
This article introduces a highly parallel algorithm for molecular dynamics simulations with short-range forces on single node multi- and many-core systems. The algorithm is designed to achieve high parallel speedups for strongly…
Sparse tensor programs are essential in deep learning and graph analytics, driving the need for optimized processing. To meet this demand, specialized hardware accelerators are being developed. Optimizing these programs for accelerators is…
For computational fluid dynamics (CFD) applications with a large number of grid points/cells, parallel computing is a common efficient strategy to reduce the computational time. How to achieve the best performance in the modern…
Modern large language model workloads put increasing demands on parallel compute capability and on-chip memory capacity, while also stressing fine-grained data movement and synchronization. These trends motivate exploring and designing…
This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures. Our approach employs a…
We propose a simulation-based approach for performance modeling of parallel applications on high-performance computing platforms. Our approach enables full-system performance modeling: (1) the hardware platform is represented by an abstract…
This paper presents a parallel Monte Carlo simulation based performance quantification method for nonlinear model predictive control (NMPC) in closed-loop. The method provides distributions for the controller performance in stochastic…
When MPI-parallel simulations run on shared Kubernetes clusters, conventional CPU scheduling leaves the vast majority of provisioned cycles idle at synchronization barriers. This paper presents a multiplexing framework that reclaims this…
We consider Monte Carlo simulations of classical spin models of statistical mechanics using the massively parallel architecture provided by graphics processing units (GPUs). We discuss simulations of models with discrete and continuous…