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With the growing complexity and capability of contemporary robotic systems, the necessity of sophisticated computing solutions to efficiently handle tasks such as real-time processing, sensor integration, decision-making, and control…
Although the cloud has reached a state of robustness, the burden of using its resources falls on the shoulders of programmers who struggle to keep up with ever-growing cloud infrastructure services and abstractions. As a result, state…
Over the past two decades, the cloud computing paradigm has gradually attracted more popularity due to its efficient resource usage and simple service access model. Virtualization technology is the fundamental element of cloud computing…
Most existing studies on performance prediction for virtual machines (VMs) in multi-tenant clouds are at system level and generally require access to performance counters in Hypervisors. In this work, we propose uPredict, a user-level…
The cloud computing literature provides various ways to utilise cloud services, each with a different viewpoint, focus, and mostly using heterogeneous technical-centric terms. This hinders efficient and consistent knowledge flow across the…
Quite generally, constraint-based metabolic flux analysis describes the space of viable flux configurations for a metabolic network as a high-dimensional polytope defined by the linear constraints that enforce the balancing of production…
Load balancers are pervasively used inside today's clouds to scalably distribute network requests across data center servers. Given the extensive use of load balancers and their associated operating costs, several efforts have focused on…
Recursive query processing has experienced a recent resurgence, as a result of its use in many modern application domains, including data integration, graph analytics, security, program analysis, networking and decision making. Due to the…
We investigate the feasibility of high performance scientific computation using cloud computers as an alternative to traditional computational tools. The availability of these large, virtualized pools of compute resources raises the…
The deployment of large-scale models, such as large language models (LLMs) and sophisticated image generation systems, incurs substantial costs due to their computational demands. To mitigate these costs and address challenges related to…
Fast Multipole Methods (FMM) are a fundamental operation for the simulation of many physical problems. The high performance design of such methods usually requires to carefully tune the algorithm for both the targeted physics and the…
Finite element simulations are essential in biomechanics, enabling detailed modeling of tissues and organs. However, architectural inefficiencies in current hardware and software stacks limit performance and scalability, especially for…
To increase performance and efficiency, systems use FPGAs as reconfigurable accelerators. A key challenge in designing these systems is partitioning computation between processors and an FPGA. An appropriate division of labor may be…
The majority of contemporary mobile devices and personal computers are based on heterogeneous computing platforms that consist of a number of CPU cores and one or more Graphics Processing Units (GPUs). Despite the high volume of these…
The deployment of large-scale models, such as large language models (LLMs), incurs substantial costs due to their computational demands. To mitigate these costs and address challenges related to scalability and data security, there is a…
The analysis of vast amounts of data and the processing of complex computational jobs have traditionally relied upon high performance computing (HPC) systems. Understanding these analyses' needs is paramount for designing solutions that can…
The excessive amounts of data generated by devices and Internet-based sources at a regular basis constitute, big data. This data can be processed and analyzed to develop useful applications for specific domains. Several mathematical and…
Main memory column-stores have proven to be efficient for processing analytical queries. Still, there has been much less work in the context of clusters. Using only a single machine poses several restrictions: Processing power and data…
Tabular foundation models, such as TabPFNv2 and TabICL, have recently dethroned gradient-boosted trees at the top of predictive benchmarks, demonstrating the value of in-context learning for tabular data. We introduce TabICLv2, a new…
Scientific workflow management systems like Nextflow support large-scale data analysis by abstracting away the details of scientific workflows. In these systems, workflows consist of several abstract tasks, of which instances are run in…