Related papers: Comprehensive Resource Measurement and Analysis fo…
Novel artificial intelligence (AI) technology has expedited various scientific research, e.g., cosmology, physics and bioinformatics, inevitably becoming a significant category of workload on high performance computing (HPC) systems.…
Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. While the HPC community has developed various resilience solutions, the solution space remains fragmented. There are no formal methods and…
The energy consumption of an exascale High-Performance Computing (HPC) supercomputer rivals that of tens of thousands of people in terms of electricity demand. Given the substantial energy footprint of exascale HPC systems and the…
HPC systems expose many configuration parameters that jointly drive competing objectives. Existing tools such as autotuners recommend good configurations but do not identify minimal changes for a near-miss configuration to meet a…
Cloud computing recently developed into a viable alternative to on-premises systems for executing high-performance computing (HPC) applications. With the emergence of new vendors and hardware options, there is now a growing need to…
The plethora of complex artificial intelligence (AI) algorithms and available high performance computing (HPC) power stimulates the expeditious development of AI components with heterogeneous designs. Consequently, the need for cross-stack…
The convergence of HPC and data-intensive methodologies provide a promising approach to major performance improvements. This paper provides a general description of the interaction between traditional HPC and ML approaches and motivates the…
Energy efficiency is a growing concern for modern computing, especially for HPC due to operational costs and the environmental impact. We propose a methodology to find energy-optimal frequency and number of active cores to run single-node…
High Performance Computing (HPC) applications are essential for scientists and engineers to create and understand models and their properties. These professionals depend on the execution of large sets of computational jobs that explore…
Data efficiency, despite being an attractive characteristic, is often challenging to measure and optimize for in task-oriented semantic parsing; unlike exact match, it can require both model- and domain-specific setups, which have,…
Many tools and libraries employ hardware performance monitoring (HPM) on modern processors, and using this data for performance assessment and as a starting point for code optimizations is very popular. However, such data is only useful if…
The IRIS-HEP Analysis Grand Challenge (AGC) is designed to be a realistic environment for investigating how analysis methods scale to the demands of the HL-LHC. The analysis task is based on publicly available Open Data and allows for…
Efficient data access in High-Performance Computing (HPC) systems is essential to the performance of intensive computing tasks. Traditional optimizations of the I/O stack aim to improve peak performance but are often workload specific and…
While detailed resource usage monitoring is possible on the low-level using proper tools, associating such usage with higher-level abstractions in the application layer that actually cause the resource usage in the first place presents a…
We present StatTestCalculator (STC), a new open-source statistical analysis tool designed for analysis high energy physics experiments. STC provides both asymptotic calculations and Monte Carlo simulations for computing the exact…
Today high-performance computing (HPC) platforms are still dominated by batch jobs. Accordingly, effective batch job scheduling is crucial to obtain high system efficiency. Existing HPC batch job schedulers typically leverage heuristic…
With the upcoming generation of telescopes, cluster scale strong gravitational lenses will act as an increasingly relevant probe of cosmology and dark matter. The better resolved data produced by current and future facilities requires…
The growing gap between processor and memory speeds results in complex memory hierarchies as processors evolve to mitigate such divergence by taking advantage of the locality of reference. In this direction, the BSC performance analysis…
Power efficiency is critical in high performance computing (HPC) systems. To achieve high power efficiency on application level, it is vital importance to efficiently distribute power used by application checkpoints. In this study, we…
With the growing scale and complexity of high-performance computing (HPC) systems, resilience solutions that ensure continuity of service despite frequent errors and component failures must be methodically designed to balance the…