Concept

Maxwell (microarchitecture)

Maxwell is the codename for a GPU microarchitecture developed by Nvidia as the successor to the Kepler microarchitecture. The Maxwell architecture was introduced in later models of the GeForce 700 series and is also used in the GeForce 800M series, GeForce 900 series, and Quadro Mxxx series, as well as some Jetson products, all manufactured with TSMC's 28 nm process. The first Maxwell-based products were the GeForce GTX 745 (OEM), GeForce GTX 750, and the GeForce GTX 750 Ti. Both were released on February 18, 2014, both with the chip code number GM107. Earlier GeForce 700 series GPUs had used Kepler chips with the code numbers GK1xx. First-generation Maxwell GPUs (code numbers GM10x) are also used in the GeForce 800M series and the Quadro Kxxx series. A second generation of Maxwell-based products was introduced on September 18, 2014 with the GeForce GTX 970 and GeForce GTX 980, followed by the GeForce GTX 960 on January 22, 2015, the GeForce GTX Titan X on March 17, 2015, and the GeForce GTX 980 Ti on June 1, 2015. The final and lowest spec Maxwell 2.0 card was the GTX950 released on Aug 20th, 2015. These GPUs have GM20x chip code numbers. Maxwell introduced an improved Streaming Multiprocessor (SM) design that increased power efficiency, the sixth and seventh generation PureVideo HD, and CUDA Compute Capability 5.2. The architecture is named after James Clerk Maxwell, the founder of the theory of electromagnetic radiation. The Maxwell architecture is used in the system on a chip (SOC), mobile application processor, Tegra X1. First generation Maxwell GPUs (GM107/GM108) were released as GeForce GTX 745, GTX 750/750 Ti, GTX 850M/860M (GM107) and GeForce 830M/840M (GM108). These new chips introduced few consumer-facing additional features, as Nvidia instead focused more on increasing GPU power efficiency. The L2 cache was increased from 256 KiB on Kepler to 2 MiB on Maxwell, reducing the need for more memory bandwidth. Accordingly, the memory bus was reduced from 192 bit on Kepler (GK106) to 128 bit, reducing die area, cost, and power draw.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related courses (1)
CS-307: Introduction to multiprocessor architecture
Multiprocessors are a core component in all types of computing infrastructure, from phones to datacenters. This course will build on the prerequisites of processor design and concurrency to introduce
Related lectures (6)
GPUs: Architecture and Programming
Explores GPU architecture, multithreading, SIMD processors, and CUDA programming for parallel computing.
GPUs: Introduction to CUDA
Introduces the basics of GPUs, CUDA programming, and thread synchronization for parallel computing applications.
GPUs: Architecture and Programming
Explores GPUs' architecture, CUDA programming, image processing, and their significance in modern computing, emphasizing early start and correctness in GPU programming.
Show more
Related publications (10)

High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs

Jean-Paul Richard Kneib, Markus Rexroth

Strong gravitational lensing is a powerful probe of cosmology and the dark matter distribution. Efficient lensing software is already a necessity to fully use its potential and the performance demands will only increase with the upcoming generation of tele ...
ELSEVIER2020

Mo(2)Cap(2): Real-time Mobile 3D Motion Capture with a Cap-mounted Fisheye Camera

Pascal Fua, Helge Jochen Rhodin

We propose the first real-time system for the egocentric estimation of 3D human body pose in a wide range of unconstrained everyday activities. This setting has a unique set of challenges, such as mobility of the hardware setup, and robustness to long capt ...
2019

LTRF: Enabling High-Capacity Register Files for GPUs via Hardware/Software Cooperative Register Prefetching

Babak Falsafi, Mario Paulo Drumond Lages De Oliveira, Hamid Sarbazi-Azad, Seyed Borna Ehsani

Graphics Processing Units (GPUs) employ large register files to accommodate all active threads and accelerate context switching. Unfortunately, register files are a scalability bottleneck for future GPUs due to long access latency, high power consumption, ...
2018
Show more
Related concepts (10)
Pascal (microarchitecture)
Pascal is the codename for a GPU microarchitecture developed by Nvidia, as the successor to the Maxwell architecture. The architecture was first introduced in April 2016 with the release of the Tesla P100 (GP100) on April 5, 2016, and is primarily used in the GeForce 10 series, starting with the GeForce GTX 1080 and GTX 1070 (both using the GP104 GPU), which were released on May 17, 2016, and June 10, 2016, respectively. Pascal was manufactured using TSMC's 16 nm FinFET process, and later Samsung's 14 nm FinFET process.
Kepler (microarchitecture)
Kepler is the codename for a GPU microarchitecture developed by Nvidia, first introduced at retail in April 2012, as the successor to the Fermi microarchitecture. Kepler was Nvidia's first microarchitecture to focus on energy efficiency. Most GeForce 600 series, most GeForce 700 series, and some GeForce 800M series GPUs were based on Kepler, all manufactured in 28 nm. Kepler also found use in the GK20A, the GPU component of the Tegra K1 SoC, as well as in the Quadro Kxxx series, the Quadro NVS 510, and Nvidia Tesla computing modules.
GeForce 10 series
The GeForce 10 series is a series of graphics processing units developed by Nvidia, initially based on the Pascal microarchitecture announced in March 2014. This design series succeeded the GeForce 900 series, and is succeeded by the GeForce 16 series and GeForce 20 series using the Turing microarchitecture. Pascal (microarchitecture) The Pascal microarchitecture, named after Blaise Pascal, was announced in March 2014 as a successor to the Maxwell microarchitecture.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.