相关论文: In-situ data analytics for highly scalable cloud m…
Efficient and scalable non-parametric or semi-parametric regression analysis and density estimation are of crucial importance to the fields of statistics and machine learning. However, available methods are limited in their ability to…
Communication and computation are often viewed as separate tasks. This approach is very effective from the perspective of engineering as isolated optimizations can be performed. However, for many computation-oriented applications, the main…
PROOF, the Parallel ROOT Facility, is a ROOT-based framework which enables interactive parallelism for event-based tasks on a cluster of computing nodes. Although PROOF can be used simply from within a ROOT session with no additional…
The open-source PyNX toolkit [Favre-Nicolin et al (2011) arXiv:1010.2641, Mandula et al (2016)] has been extended to provide tools for coherent X-ray imaging data analysis and simulation. All calculations can be executed on graphical…
To mitigate the ever worsening "Power wall" and "Memory wall" problems, multi-core architectures with multilevel cache hierarchies have been widely accepted in modern processors. However, the complexity of the architectures makes modeling…
Scientific workflows are widely used to automate scientific data analysis and often involve processing large quantities of data on compute clusters. As such, their execution tends to be long-running and resource intensive, leading to…
Training large language models requires extensive processing, made possible by many high-performance computing resources. This study compares multi-node and multi-GPU environments for training large language models of electrocardiograms. It…
Coupled AI-Simulation workflows are becoming the major workloads for HPC facilities, and their increasing complexity necessitates new tools for performance analysis and prototyping of new in-situ workflows. We present SimAI-Bench, a tool…
Cloud computing has become the ubiquitous computing and storage paradigm. It is also attractive for scientists, because they do not have to care any more for their own IT infrastructure, but can outsource it to a Cloud Service Provider of…
Much like on-premises systems, the natural choice for running database analytics workloads in the cloud is to provision a cluster of nodes to run a database instance. However, analytics workloads are often bursty or low volume, leaving…
In the past few years, datacenter (DC) energy consumption has become an important issue in technology world. Server consolidation using virtualization and virtual machine (VM) live migration allows cloud DCs to improve resource utilization…
Input data preprocessing is a common bottleneck when concurrently training multimedia machine learning (ML) models in modern systems. To alleviate these bottlenecks and reduce the training time for concurrent jobs, we present Seneca, a data…
On-chip communication infrastructure is a central component of modern systems-on-chip (SoCs), and it continues to gain importance as the number of cores, the heterogeneity of components, and the on-chip and off-chip bandwidth continue to…
Co-clustering simultaneously clusters rows and columns, revealing more fine-grained groups. However, existing co-clustering methods suffer from poor scalability and cannot handle large-scale data. This paper presents a novel and scalable…
Photonic computing shows promise for transformative advancements in machine learning (ML) acceleration, offering ultra-fast speed, massive parallelism, and high energy efficiency. However, current photonic tensor core (PTC) designs based on…
Many applications in important problem domains such as machine learning and computer vision are streaming applications that take a sequence of inputs over time. It is challenging to find knob settings that optimize the run-time performance…
Read-optimized columnar databases use differential updates to handle writes by maintaining a separate write-optimized delta partition which is periodically merged with the read-optimized and compressed main partition. This merge process…
HPC-based applications often have complex workflows with many software dependencies that hinder their portability on contemporary HPC architectures. In addition, these applications often require extraordinary efforts to deploy and execute…
Energy efficiency is one of the major concern in designing advanced computing infrastructures. From single nodes to large-scale systems (data centers), monitoring the energy consumption of the computing system when applications run is a…
Experimental science is increasingly driven by instruments that produce vast volumes of data and thus a need to manage, compute, describe, and index this data. High performance and distributed computing provide the means of addressing the…