Charles Humble

3 weeks ago | thenewstack.io | Charles Humble

While detailed instrumentation of CPUs has been available for some time, GPUs are still commonly treated as black boxes. Zymtrace, a new profiler designed to provide detailed introspection for both CPUs and NVIDIA GPUs, as well as the interactions between them, aims to solve this problem. It comes from zystem, a young startup and member of the NVIDIA Inception program on a mission to accelerate the world’s transition to cost-efficient and sustainable computing.

1 month ago | thenewstack.io | Charles Humble

Cloud colossus Amazon Web Services has released an update to its much-criticized Customer Carbon Footprint Tool (CCFT), which has received little attention since its original release in 2022. Adrian Cockcroft, who was head of sustainability at AWS at the time, freely acknowledged to The New Stack in an interview that that first release was “pretty woeful.”“It was built by a team in the energy organization who provided power to the data centers,” said Cockcroft, an occasional TNS contributor.

1 month ago | thenewstack.io | Charles Humble

As demand for AI surges, figuring out the performance of proprietary AI models is only half the battle. The other half: reckoning with how much energy AI demands. By 2030 the U.S. alone is set to consume more electricity for data centers than for the production of aluminum, steel, cement, chemicals and all other energy-intensive goods combined, according to a sweeping report released in April by the International Energy Agency (IEA).

2 months ago | thenewstack.io | Charles Humble

Disclosure: The author wrote this post on behalf of cycle.io. Cycle, the lowops Kubernetes alternative, has announced two new features that take it from cloud container orchestration to being a comprehensive compute platform for containers, virtual machines (VMs), and functions that you can run anywhere. Cycle combines two things: Platform orchestration and infrastructure management.

2 months ago | thenewstack.io | Charles Humble

The classical machine learning paradigm requires the aggregation of user data in a central location where data scientists can pre-process it, calculate features, tune models and evaluate performance. The advantages of this approach include being able to leverage high-performance hardware (such as GPUs), and the scope for a data science team to perform in-depth data analysis to improve model performance.