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As more High-Performance Computing (HPC) and Deep Learning (DL) applications are adapting to scale using GPUs, the communication of GPU-resident data is becoming vital to end-to-end application performance. Among the available MPI operations in ...

Applications running on large scale systems often suffer from degraded performance and lack of reproducible run-times due to network-level congestion, whether caused by the application network traffic itself, or by unrelated background network ...

As emerging workloads exhibit irregular memory access patterns with poor data reuse and locality, they would benefit from a DRAM that achieves low latency without sacrificing bandwidth and energy efficiency. We propose LLM (Low Latency Memory), a ...$$$$$$

SU3_Bench explores performance portability across multiple programming models using a simple but nontrivial mathematical kernel. This kernel has been derived from the [inline-graphic not available: see fulltext] (LQCD) code used in applications ...

HPC system users depend on profiling and analysis tools to obtain insights into the performance of their applications and tweak them. The complexity of modern HPC systems have necessitated advances in the associated HPC tools making them equally ...

Recent advances in High Performance Computing (HPC) enable Deep Learning (DL) models to achieve state-of-the-art performance by exploiting multiple processors. Data parallelism is a strategy that replicates the DL model on each processor, which is ...$\mathrm{}$$\mathrm{}$$$$$

This contribution presents an efficient algorithm for resolving hanging-node constraints on the fly for high-order finite-element computations on adaptively refined meshes, using matrix-free implementations. We concentrate on unstructured hex-...$\mathrm{}$

Load balancing of generic wave equation solvers over dynamically adaptive meshes with local time stepping is difficult, as the load changes with every time step. Task-based programming promises to mitigate the load balancing problem. We study a ...

Inspired by quantum annealing, simulated quantum annealing (SQA) mimics quantum tunneling effects on classical computers to perform annealing through a path-integral Monte Carlo simulation, which increases the potential to find the global optima ...

The task of multi-dimensional numerical integration is frequently encountered in physics and other scientific fields, e.g., in modeling the effects of systematic uncertainties in physical systems and in Bayesian parameter estimation . Multi-...

Call graphs generated by profiling tools are critical to dissecting the performance of parallel programs. Although many mature and sophisticated profiling tools record call graph data, each tool is different in its runtime overheads, memory ...

Application memory access patterns are crucial in deciding how much traffic is served by the cache and forwarded to the dynamic random-access memory (DRAM). However, predicting such memory traffic is difficult because of the interplay of ...

As the complexity of compute nodes in high-performance computing (HPC) keeps increasing, systems equipped with heterogeneous memory devices are becoming paramount. Efficiently utilizing heterogeneous memory-based systems, however, poses ...

Frameworks for Distributed Deep Learning (DDL) have become popular alternatives to distribute training by adding a few lines of code to a single-node script. From a High-Performance Computing (HPC) perspective, traditional profiling tools for ...

In this paper, we investigate the applicability of reinforcement learning as a possible approach to select code variants. Our approach is based on the observation that code variants are usually convertible between one another by code ...

OpenMP has a long and successful history in parallel programming for CPUs. Since the introduction of accelerator offloading, it has evolved into a promising candidate for all intra-node parallel computing needs. While this addition broke with the ...

Krylov subspace solvers such as GMRES and preconditioners such as incomplete LU (ILU) are the most commonly used methods to solve general-purpose, large-scale linear systems in simulations efficiently. Parallel Krylov subspace solvers and ...

Delivering a reproducible environment along with complex and up-to-date software stacks on thousands of distributed and heterogeneous worker nodes is a critical task. The CernVM-File System (CVMFS) has been designed to help various communities to ...

In an older version of this paper, the name of the fourth author was missing. This has been corrected.