The growing disparity between computational power and on-chip communication bandwidth is a critical bottleneck in modern Systems-on-Chip (SoCs), especially for data-parallel workloads like AI. Efficient point-to-multipoint (P2MP) data movement, such...
A range of RISC-V based accelerators are available and coming to market, and there is strong potential for these to be used for High Performance Computing (HPC) workloads. However, such accelerators tend to provide bespoke programming models and APIs...
Highly parallelized workloads like machine learning training, inferences and general HPC tasks are greatly accelerated using GPU devices. In a cloud computing cluster, serving a GPU's computation power through multi-tasks sharing is highly demanded s...
This community is for engineers and builders working with AI infrastructure, GPU workloads, and inference at scale. Topics include deployment tradeoffs, performance optimization, cost efficiency, and...
Points: 216 | Comments: 120 | Author: sorenbs
Modern edge-cloud systems face challenges in efficiently scaling resources to handle dynamic and unpredictable workloads. Traditional scaling approaches typically rely on static thresholds and predefined rules, which are often inadequate for optimizi...
News News: US schools are increasingly using AI to enhance learning and ease teacher workloads, but must adhere to federal guidelines emphasizing privacy, equity
Learn how to create a modern analytics architecture by using Azure Databricks and Data Lake Storage. Unify data, analytics, and AI workloads at any scale.
Learn how to enable priority processing for Microsoft Foundry models to achieve low latency and high availability for time-sensitive workloads.
Learn more about the key advantages of Azure and how it is the optimal cloud for running Databricks workloads.
Find architecture diagrams and technology descriptions for reference architectures, real world examples of cloud architectures, and solution ideas for common workloads on Azure.
Find architecture diagrams and technology descriptions for reference architectures, real world examples of cloud architectures, and solution ideas for common workloads on Azure.
The best security for Google Cloud. Reduce risk and secure cloud, generative AI, and agentic AI workloads.
Easy to use data protection for Google Cloud workloads. Manage backups for multiple applications and databases, all in one place.
Google's cloud-based block storage includes Hyperdisk, Persistent Disk, and Local SSD. Block storage is best suited for analytics, databases, SAP applications, high performance computing, and stateless workloads.
Host workloads on hardware dedicated to your projects.
Migrate and run your VMware workloads natively in Google Cloud. Benefit from increased scale and agility while maintaining operational continuity.
Google Cloud Serverless for Apache Spark offers quick VM startup and dynamic autoscaling for the interactive, batch, and AI workloads.
Migrate to Virtual Machines gets enterprise workloads running on Google Cloud quickly, safely, and with minimal risk and downtime.
Bare Metal Solution for Oracle provides hardware to run specialized workloads with low latency to Google Cloud.