Related papers: ATLAHS: An Application-centric Network Simulator T…
Efficient hyperparameter or architecture search methods have shown remarkable results, but each of them is only applicable to searching for either hyperparameters (HPs) or architectures. In this work, we propose a unified pipeline, AutoHAS,…
Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning…
The computing systems used by LHC experiments has historically consisted of the federation of hundreds to thousands of distributed resources, ranging from small to mid-size resource. In spite of the impressive scale of the existing…
Monitoring of the large-scale data processing of the ATLAS experiment includes monitoring of production and user analysis jobs. The Experiment Dashboard provides a common job monitoring solution, which is shared by ATLAS and CMS…
The DataFlow is sub-system of the ATLAS data acquisition responsible for the reception, buffering and subsequent movement of partial and full event data to the higher level triggers: Level 2 and Event Filter. The design of the software is…
With the great success of Deep Neural Networks (DNN), the design of efficient hardware accelerators has triggered wide interest in the research community. Existing research explores two architectural strategies: sequential layer execution…
Multi-tenant machine learning services have become emerging data-intensive workloads in data centers with heavy usage of GPU resources. Due to the large scale, many tuning parameters and heavy resource usage, it is usually impractical to…
Machine learning (ML) applications become increasingly common in many domains. ML systems to execute these workloads include numerical computing frameworks and libraries, ML algorithm libraries, and specialized systems for deep neural…
This work describes the design, implementation and performance analysis of a distributed two-tiered storage software. The first tier functions as a distributed software cache implemented using solid-state devices~(NVMes) and the second tier…
Next-generation real-time compute-intensive applications, such as extended reality, multi-user gaming, and autonomous transportation, are increasingly composed of heterogeneous AI-intensive functions with diverse resource requirements and…
Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for both experimentation and production deployment). These usually…
Massive upgrades to science infrastructure are driving data velocities upwards while stimulating adoption of increasingly data-intensive analytics. While next-generation exascale supercomputers promise strong support for I/O-intensive…
Training machine learning (ML) models with large datasets can incur significant resource contention on shared clusters. This training typically involves many iterations that continually improve the quality of the model. Yet in exploratory…
Modern storage systems often combine fast cache with slower backend devices to accelerate I/O. As performance gaps narrow, concurrently accessing both devices, rather than relying solely on cache hits, can improve throughput. However, in…
Due to decelerating gains in single-core CPU performance, computationally expensive simulations are increasingly executed on highly parallel hardware platforms. Agent-based simulations, where simulated entities act with a certain degree of…
Virtual testing has emerged as an effective approach to accelerate the deployment of automated driving systems. Nevertheless, existing simulation toolchains encounter difficulties in integrating rapid, automated scenario generation with…
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…
This paper investigates the network load balancing problem in data centers (DCs) where multiple load balancers (LBs) are deployed, using the multi-agent reinforcement learning (MARL) framework. The challenges of this problem consist of the…
As a current trend in Artificial Intelligence (AI), large foundation models are increasingly employed as the core of AI services. However, even after training, serving such models at scale remains a challenging task due to their heavy…
Hashkat (http://hashkat.org) is a free, open source, agent based simulation software package designed to simulate large-scale online social networks (e.g. Twitter, Facebook, LinkedIn, etc). It allows for dynamic agent generation, edge…