More than anything else, over its long history in the computing business, IBM has been a platform company and say what you will about the woes it has had through several phases of its history, what seems obvious is that when Big Blue forgets this it runs into trouble. …
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. …
This month Nvidia bolstered its GPU strategy to stretch further into deep learning, high performance computing, and other markets, and while there are new options to consider, particularly for the machine learning set, it is useful to understand what these new arrays of chips and capabilities mean for users at scale. …
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. …
Chip maker Intel has been getting a lot of grief in recent days about missing the boat on putting chips in Apple’s iPhone back when the product was announced back in 2007, and then subsequently also losing out on the opportunity to have Intel Inside the Apple iPad tablet that came out three years later. …
Some techies are capable of writing programs in assembler, but all will agree that they are very glad that they don’t need to. …
Nvidia made a lot of big bets to bring its “Pascal” GP100 GPU to market and its first implementation of the GPU is aimed at its Tesla P100 accelerator for radically improving the performance of massively parallel workloads like scientific simulations and machine learning algorithms. …
The Power9 processor that IBM is working on in conjunction with hyperscale and HPC customers could be the most important chip that Big Blue has brought to market since the Power4 processor back in 2001. …
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. …
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