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Virtual machines and virtualized hardware have been around for over half a century. The commoditization of the x86 platform and its rapidly growing hardware capabilities have led to recent exponential growth in the use of virtualization…
Measuring performance-critical characteristics of application workloads is important both for developers, who must understand and optimize the performance of codes, as well as designers and integrators of HPC systems, who must ensure that…
Machine learning inference is increasingly being executed locally on mobile and embedded platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we present approaches for online resource management in…
FPGAs have found increasing adoption in data center applications since a new generation of high-level tools have become available which noticeably reduce development time for FPGA accelerators and still provide high quality of results.…
Experiment-in-the-Loop Computing (EILC) requires support for numerous types of processing and the management of heterogeneous infrastructure over a dynamic range of scales: from the edge to the cloud and HPC, and intermediate resources.…
Autoscaling has become a baseline expectation for cloud-native big data processing, and the design space has expanded beyond rule-based heuristics to include learned controllers and, most recently, large language model (LLM) agents. Yet…
Blockchains are gaining momentum due to the interest of industries and people in \emph{decentralized applications} (Dapps), particularly in those for trading assets through digital certificates secured on blockchain, called tokens. As a…
Large language models (LLMs) are becoming increasingly capable at small parameter scales. At the same time, conventional cloud-centric deployment introduces challenges around data privacy, latency, and cost that are acute in operational…
The ability to understand how a scientific application is executed on a large HPC system is of great importance in allocating resources within the HPC data center. In this paper, we describe how we used system performance data to identify:…
The increasing demands for computing performance have been a reality regardless of the requirements for smaller and more energy efficient devices. Throughout the years, the strategy adopted by industry was to increase the robustness of a…
The performance of data intensive applications is often dominated by their input/output (I/O) operations but the I/O stack of systems is complex and severely depends on system specific settings and hardware components. This situation makes…
I would like to share recommendations on how to do performance benchmarks for the purpose of computer science research evaluation. Research in my field (programming language research) often involves performance considerations, but it is…
The deployment of large language models (LLMs) in production environments has created an urgent need for observability systems that span the full stack -- from model internals to GPU kernels. Yet existing monitoring approaches address…
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…
Distributed Software Systems are used these days by many people in the real time operations and modern enterprise applications. One of the most important and essential attributes of measurements for the quality of service of distributed…
Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a…
This paper is based on and advocates the trend in software engineering of extending the use of software patterns as means of structuring solutions to software development problems (be they motivated by best practice or by company interests…
Important computational physics problems are often large-scale in nature, and it is highly desirable to have robust and high performing computational frameworks that can quickly address these problems. However, it is no trivial task to…
Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental concerns. Yet, progress on model efficiency has been impeded…
Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance. It typically allows the main flow of applications to be executed on a CPU while the most computationally…