July 12, 2023
Editor’s note; In light of recent updates to Google’s Privacy Policy, “For example, we use publicly available information to help train Google’s AI mo Read more…
April 27, 2022
In a one-two punch of new HPC-backed AI announcements, Hewlett Packard Enterprise (HPE) today announced its new Machine Learning Development System (MLDS) and S Read more…
January 26, 2022
Earlier this month, the White House Office of Science and Technology Policy (OSTP) Scientific Integrity Task Force released a report titled “Protecting the In Read more…
October 15, 2021
With more and more enterprises turning to AI for a myriad of tasks, companies quickly find out that training AI models is expensive, difficult and time-consuming. Finding a new approach to deal with those cascading challenges is the aim of a new startup, MosaicML, that just came out of stealth... Read more…
September 27, 2021
Making sense of ML performance and benchmark data is an ongoing challenge. In light of last week’s release of the most recent MLPerf (v1.1) inference results, now is perhaps a good time to review how valuable (or not) such ML benchmarks are and the challenges they face. Two researchers... Read more…
September 3, 2021
As datasets get larger and larger, the potential of machine learning insights from those datasets grows correspondingly immense – but bottlenecks in computing Read more…
August 27, 2021
Esperanto Technologies made waves last December when it announced ET-SoC-1, a new RISC-V-based chip aimed at machine learning that packed nearly 1,100 cores onto a package small enough to fit six times over on a single PCIe card. Now, Esperanto is back, silicon in-hand and taking aim... Read more…
August 7, 2021
For nearly a hundred years, German-based firm Festo has delivered industrial controls and automation tools to its clients, growing to over 20,000 employees and Read more…
Data centers are experiencing increasing power consumption, space constraints and cooling demands due to the unprecedented computing power required by today’s chips and servers. HVAC cooling systems consume approximately 40% of a data center’s electricity. These systems traditionally use air conditioning, air handling and fans to cool the data center facility and IT equipment, ultimately resulting in high energy consumption and high carbon emissions. Data centers are moving to direct liquid cooled (DLC) systems to improve cooling efficiency thus lowering their PUE, operating expenses (OPEX) and carbon footprint.
This paper describes how CoolIT Systems (CoolIT) meets the need for improved energy efficiency in data centers and includes case studies that show how CoolIT’s DLC solutions improve energy efficiency, increase rack density, lower OPEX, and enable sustainability programs. CoolIT is the global market and innovation leader in scalable DLC solutions for the world’s most demanding computing environments. CoolIT’s end-to-end solutions meet the rising demand in cooling and the rising demand for energy efficiency.
Divergent Technologies developed a digital production system that can revolutionize automotive and industrial scale manufacturing. Divergent uses new manufacturing solutions and their Divergent Adaptive Production System (DAPS™) software to make vehicle manufacturing more efficient, less costly and decrease manufacturing waste by replacing existing design and production processes.
Divergent initially used on-premises workstations to run HPC simulations but faced challenges because their workstations could not achieve fast enough simulation times. Divergent also needed to free staff from managing the HPC system, CAE integration and IT update tasks.
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