In the previous article, we left off with the basic storage model having its objects first existing as changed in the processor’s cache, then being aged into volatile DRAM memory, often with changes first logged synchronously into I/O-based persistent storage, and later with the object’s changes proper later copied from volatile memory into persistent storage. …
Pattern analytics, deep learning, and machine learning have fueled a rapid rise in interest in GPU computing, in addition to GPU computing applications in high performance computing (HPC) and cloud-based data analytics. …
Some techies are capable of writing programs in assembler, but all will agree that they are very glad that they don’t need to. …
Supercomputer maker Cray might not roll out machines for deep learning anytime in 2016, but like other system vendors with deep roots in high performance computing, which leverages many of the same hardware elements (strong interconnect and GPU acceleration, among others), they are seeing how to loop their expertise into a future where machine learning rules. …
Deep learning could not have developed at the rapid pace it has over the last few years without companion work that has happened on the hardware side in high performance computing. …
Absorbing a collection of new processing, memory, storage, and networking technologies in a fast fashion on a complex system is no easy task for any system maker or end user creating their own infrastructure, and it takes time even for a big company like Oracle to get all the pieces together and weld them together seamlessly. …
As readers of The Next Platform are well aware, Hewlett Packard Enterprise is staking a lot of the future of its systems business on The Machine, which embodies the evolving concepts for disaggregated and composable systems that are heavy on persistent storage that sometimes functions like shared memory, on various kinds of compute, and on the interconnects between the two. …
There are two things that underdogs have to do to take a big bite out of a market. …
Whenever companies that sell compute components for a living get into the systems business, they usually have a good reason for doing so. …
With machine learning taking off among hyperscalers and others who have massive amounts of data to chew on to better serve their customers and traditional simulation and modeling applications scaling better across multiple GPUs, all server makers are in an arm’s race to see how many GPUs they can cram into their servers to make bigger chunks of compute available to applications. …
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