Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Real-time monitoring of rainfall through rain gauges enables operations personnel to be alerted about potential flooding or sewer overflow situations. By closely tracking and evaluating precipitation levels at various sites, it becomes possible to estimate rainfall in areas that are not directly monitored, a method known as the “Distributed Rainfall Modelling Technique” (DRMT). The integration of rainfall radar data with readings from rain gauges facilitates the creation of enhanced rainfall coverage maps. Furthermore, examining historical rainfall data helps in constructing rainfall intensity-duration curves, which can be compared to the design intensity-duration-frequency curves of the region, aiding in the identification of return periods for recorded events through forensic analysis. In addition, new intensity-duration-frequency curves can be generated to inform the design of drainage infrastructure, including sewers, channels, and storage facilities. Continuous flow monitoring, coupled with data analysis, contributes to the development of rainfall versus stormwater runoff response curves, which are essential for calibrating drainage system models effectively. This comprehensive approach ensures that urban planning and flood management strategies are well-informed and responsive to actual conditions.
Description
Hammerspace innovatively leverages the local NVMe storage embedded within GPU servers, converting it into a high-performance, shared storage tier designed specifically for large-scale AI training and checkpointing workloads. This approach eliminates bottlenecks inherent in legacy storage systems that struggle to keep GPUs fully utilized, while significantly reducing power consumption and external storage expenses. The platform’s parallel file system architecture supports massive scalability, allowing data to be served simultaneously to thousands of GPU nodes with minimal latency. Hammerspace integrates seamlessly with existing Linux storage servers and supports hybrid cloud environments, enabling data orchestration between on-premises and cloud infrastructure. It delivers record-setting performance validated by MLPerf benchmarks, proving its efficiency for demanding machine learning workloads. Customers such as Meta and Los Alamos National Laboratory trust Hammerspace to optimize their AI data pipelines and infrastructure investments. With quick setup and intuitive management, Hammerspace helps organizations accelerate AI projects while reducing operational complexity. By transforming underutilized storage into a powerful resource, Hammerspace drives cost savings and faster innovation.
API Access
Has API
API Access
Has API
Integrations
Amazon Web Services (AWS)
Azure Marketplace
Google Cloud Platform
Microsoft Azure
Integrations
Amazon Web Services (AWS)
Azure Marketplace
Google Cloud Platform
Microsoft Azure
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Smart City Water
Country
Canada
Website
smartcitywater.ca/datacurrent/
Vendor Details
Company Name
Hammerspace
Founded
2018
Country
United States
Website
hammerspace.com
Product Features
Product Features
Data Management
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge
Disaster Recovery
Administration Policies
Bare-Metal Recovery
Encryption
Failover Testing
Flexible Data Capture
Multi-Platform Support
Multiple Data Type Support
Offline Storage