The accuracy requirements in many scientific computing workloads result in the use of double-precision floating-point arithmetic in the execution kernels. Nevertheless, emerging real-number representations, such as posit arithmetic, show promise in d...
Designers of modern reader-writer locks confront a difficult trade-off related to reader scalability. Locks that have a compact memory representation for active readers will typically suffer under high intensity read-dominated workloads when the "rea...
The CMS Submission Infrastructure (SI) is the main computing resource provisioning system for CMS workloads. A number of HTCondor pools are employed to manage this infrastructure, which aggregates geographically distributed resources from the WLCG an...
**Objective:** Build a cheap, kick-ass server for not a whole lot of money. Server must also has a clear upgrade path for future expansion, and be able to perform in a variety of workloads. **Rules ...
This paper releases and analyzes a month-long trace of 85 billion user requests and 11.9 million cold starts from Huawei's serverless cloud platform. Our analysis spans workloads from five data centers. We focus on cold starts and provide a comprehen...
This sample explains how you can create a zone redundant AKS cluster and the implications of each approach on the deployment strategy and configuration of the persistent volumes used by the workloads. (⭐ 4)
Designing low-latency cloud-based applications that are adaptable to unpredictable workloads and efficiently utilize modern cloud computing platforms is hard. The actor model is a popular paradigm that can be used to develop distributed applications:...
BharatMLStack is an open-source, end-to-end machine learning infrastructure stack built at Meesho to support real-time and batch ML workloads at Bharat scale (⭐ 672)
Code for "Heterogenity-Aware Cluster Scheduling Policies for Deep Learning Workloads", which appeared at OSDI 2020 (⭐ 137)
In-network computation represents a transformative approach to addressing the escalating demands of Artificial Intelligence (AI) workloads on network infrastructure. By leveraging the processing capabilities of network devices such as switches, route...
The insatiable appetite of Artificial Intelligence (AI) workloads for computing power is pushing the industry to develop faster and more efficient accelerators. The rigidity of custom hardware, however, conflicts with the need for scalable and versat...
Redpanda Console is a developer-friendly UI for managing your Kafka/Redpanda workloads. Console gives you a simple, interactive approach for gaining visibility into your topics, masking data, managing consumer groups, and exploring real-time data with time-tra…
This paper describes an automated approach to handling Big Data workloads on HPC systems. We describe a solution that dynamically creates a unified cluster based on YARN in an HPC Environment, without the need to configure and allocate a dedicated Ha...
Imagine activating new robots meant to aid staff in an elder care facility, only to discover the robots are counterproductive. They undermine the most meaningful moments of the jobs and increase staff workloads, because robots demand care too. Eventu...
OpenBot leverages smartphones as brains for low-cost robots. We have designed a small electric vehicle that costs about $50 and serves as a robot body. Our software stack for Android smartphones supports advanced robotics workloads such as person following and…
BladeDISC is an end-to-end DynamIc Shape Compiler project for machine learning workloads. (⭐ 918)
>Cobalt 200 is a milestone in our continued approach to optimize every layer of the cloud stack from silicon to software. Our design goals were to deliver full compatibility for workloads using our...
Cloud computing has revolutionized the way organizations manage their IT infrastructure, but it has also introduced new challenges, such as managing cloud costs. The rapid adoption of artificial intelligence (AI) and machine learning (ML) workloads h...
Cost monitoring for Kubernetes workloads and cloud costs (⭐ 6414)
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compared to similarly-sized models. Nemotron-Nano-9B-v2 builds on the Nemotr...