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File system checking and recovery (C/R) tools play a pivotal role in increasing the reliability of storage software, identifying and correcting file system inconsistencies. However, with increasing disk capacity and data content, file…
Performance regressions in large-scale software systems can lead to substantial resource inefficiencies, making their early detection critical. Frequent benchmarking is essential for identifying these regressions and maintaining…
Analyzing large-scale graphs provides valuable insights in different application scenarios. While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the tasks of profiling…
Estimating power consumption in modern Cloud environments is essential for carbon quantification toward green computing. Specifically, it is important to properly account for the power consumed by each of the running applications, which are…
Many-core accelerators, as represented by the XeonPhi coprocessors and GPGPUs, allow software to exploit spatial and temporal sharing of computing resources to improve the overall system performance. To unlock this performance potential…
In Cloud Computing platforms the addition of hardware monitoring devices to gather power usage data can be impractical or uneconomical due to the large number of machines to be metered. CloudMonitor, a monitoring tool that can generate…
Exploiting the performance of today's processors requires intimate knowledge of the microarchitecture as well as an awareness of the ever-growing complexity in thread and cache topology. LIKWID is a set of command-line utilities that…
Edge computing has emerged as a pivotal technology, offering significant advantages such as low latency, enhanced data security, and reduced reliance on centralized cloud infrastructure. These benefits are crucial for applications requiring…
Pipeline parallelism is widely used to scale the training of transformer-based large language models, various works have been done to improve its throughput and memory footprint. In this paper, we address a frequently overlooked issue: the…
Modern Out-of-Order (OoO) CPUs are complex systems with many components interleaved in non-trivial ways. Pinpointing performance bottlenecks and understanding the underlying causes of program performance issues are critical tasks to fully…
The paper is devoted to studying the performance of a computational pipeline, the number of simultaneously executing stages of which at each time is bounded from above by a fixed number. A look at the restriction as a structural hazard…
In this work, we propose KPerfIR, a novel multilevel compiler-centric infrastructure to enable the development of customizable, extendable, and portable profiling tools tailored for modern artificial intelligence (AI) workloads on modern…
The efficiency of current cargo screening processes at sea and air ports is largely unknown as few benchmarks exists against which they could be measured. Some manufacturers provide benchmarks for individual sensors but we found no…
Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have…
Virtualization enables information and communications technology industry to better manage computing resources. In this regard, improvements in virtualization approaches together with the need for consistent runtime environment, lower…
Memory profiling captures programs' dynamic memory behavior, assisting programmers in debugging, tuning, and enabling advanced compiler optimizations like speculation-based automatic parallelization. As each use case demands its unique…
Scheduling query execution plans is a particularly complex problem in shared-nothing parallel systems, where each site consists of a collection of local time-shared (e.g., CPU(s) or disk(s)) and space-shared (e.g., memory) resources and…
Pulsar data analysis pipelines have historically been comprised of bespoke software systems, supporting the off-line analysis of data. However modern data acquisition systems are making off-line analyses impractical. They often output…
The aim of this paper is to develop an approach to visualizations that benefits from distributed computing. Three schemes of process distribution are considered: parallel, pipeline, and expanding pipeline computations. Expanding pipeline…
Data centers have become center of big data processing. Most programs running in a data center processes big data. The storage requirements of such programs cannot be fulfilled by a single node in the data center, and hence a distributed…