Building a quantum computer that can outperform conventional systems on certain types of algorithms looks to be tantalizingly close. As it stands today, Google and IBM appear to be the most likely candidates to claim that achievement.
The lack of memory capacity in traditional computer clusters is a significant limitation to application performance in the datacenter. A new memory disaggregation technology developed at the University of Michigan is designed to help alleviate this critical obstacle.
The FY 2018 Congressional budget request for the Department of Energy has been released, reflecting a White House that favors supercomputing infrastucture over scientific research. That turns out to be both good news and bad news for the HPC community.
At Microsoft’s recent Build conference, Azure CTO Mark Russinovich presented a future that would significantly expand the role of FPGAs in their cloud platform. Some of these plans could sideline the use of GPUs and CPUs used for deep learning from the likes of NVIDIA, Intel, and other chipmakers.
Cray is now offering its Urika-GX supercomputer for rent. One of the last HPC system vendors to give cloud computing a whirl, the company’s initial foray into supercomputer-as-a-service will target life science customers looking for compute cycles on something more sophisticated than a traditional cluster.
Intel has demonstrated its much-anticipated non-volatile DIMMs based on its 3D XPoint technology. The chipmaker is claiming this product will revolutionize the storage hierarchy by enabling terascale-sized datasets to be brought into the memory subsystem of conventional servers.
In a blog post, penned by Google veterans Jeff Dean and Urs Hölzle, the company announced it has developed and deployed its second-generation Tensor Processing Units (TPUs). The newly hatched TPU is being used to accelerate Google’s machine learning work and will also become the basis of a new cloud service.