What changed between the past four GPU generations is the size of Nvidia as a company. If we look into the past, GPU architectures such as Fermi, Kepler or Maxwell all had to compromise between demands of different product lines: GeForce for gamers, Quadro for visualization, Tegra for embedded (automotive/tablets), and Tesla for computation.
And if you’re doing search optimization or simulation, you want a different architecture that does not look anything like high-performance, low-power CMOS.” “For machine learning, we’ve been running those as convolutional neural networks on GPUs, which is inefficient. “We are going to define different application domains, and in those domains there will be very different selection criteria for architectures and devices,” said Tom Conte, co-chair of IEEE’s Rebooting Computing Initiative and a professor of computer science and electrical and computer engineering at Georgia Tech. Pascal GPU architecture marks the departure of Nvidia from ‘one fits all’ into an application specific silicon, fitting with the industry trend: While the performance results are under NDA until the May 17th (expect a tidal wave of reviews from usual suspects), we are now digging into the architecture that makes GeForce GTX 1080 ‘a screamer’.
Both cards set to offer record-breaking performance per watt, and that performance enabled Nvidia to price the parts above its predecessors. In this article, we will analyze the key elements that make second Pascal chip (GP104) even more efficient than the GP100 (Tesla P100). At the inaugural edition of North American Dreamhack conference, Electronic Arts and DICE launched Battlefield 1, while Nvidia unveiled their first Pascal-based consumer cards, the GeForce GTX 10.
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