8K movie is the next big thing and an announcement by RED and NVIDIA is making editing such movies a reality. The partners have introduced the NVIDIA CUDA-accelerated REDCODE RAW decode SDK that gives software developers and studios a powerful new way to work with 8K video. Continue reading “Making 8K movie editing a reality”
It’s no dinosaur but the newly-announced NVIDIA Titan RTX, dubbed T-Rex, is certainly very powerful — to the tune of 130 teraflops of deep learning performance and 11 GigaRays of ray-tracing performance. Continue reading “NVIDIA unleashes powerful ‘T-Rex’ GPU”
NVIDIA is among a group of investors led by Chinese social media company Sina investing more than US$20 million in Chinese startup TuSimple.
Formed in 2015, TuSimple has more than 100 employees in R&D centres in Beijing and San Diego developing technology for autonomous long-distance freight delivery. It uses NVIDIA GPUs, NVIDIA DRIVE PX 2, Jetson TX2, CUDA, TensorRT, and cuDNN to develop its autonomous driving solution.
In June, the company successfully completed a 200-mile Level 4 test drive from San Diego to Yuma, Arizona, using NVIDIA GPUs and cameras as the primary sensor.
NVIDIA is investing in Deep Instinct, an Israeli-based startup that uses deep learning to thwart cyber attacks.
Deep Instinct uses a GPU-based neural network and CUDA to achieve 99 percent detection rates, compared with about 80 percent detection from conventional cyber security software. Its software can automatically detect and defeat the most advanced cyber attacks.
“Deep Instinct is an emerging leader in applying GPU-powered AI through deep learning to address cybersecurity, a field ripe for disruption as enterprise customers migrate away from traditional solutions. We’re excited to work together with Deep Instinct to advance this important field,” said Jeff Herbst, Vice President of Business Development of NVIDIA.
NVIDIA has updated its GPU-accelerated deep learning software that will double deep learning training performance.
With the new software, data scientists and researchers can supercharge their deep learning projects and product development work by creating more accurate neural networks through faster model training and more sophisticated model design.
The NVIDIA DIGITS Deep Learning CPU Training System version 2 (DIGITS 2) and NVIDIA CUDA Deep Neural Network library version 3 (cuDNN 3) provide significant performance enhancements and new capabilities.
NVIDIA unveiled at CES the Tegra K1 mobile processor, a 192-core super chip featuring the same NVIDIA Kepler architecture that powers the NVIDIA GeForce GTX 780 Ti. For the first time, next-generation PC gaming will now be available on mobile platforms.
The Tegra K1 processor sets new mobile standards by supporting the latest PC-class gaming technologies, enabling it to run sophisticated gaming engines like Epic Games’ Unreal Engine 4. It delivers advanced computation capabilities to speed the development of applications for computer vision and speech recognition. And its extraordinary efficiency delivers higher performance than any other mobile GPU at the same power level.
“Over the past two decades, NVIDIA invented the GPU and has developed more graphics technologies than any other company,” said Jen-Hsun Huang, co-founder and CEO, NVIDIA. “With Tegra K1, we’re bringing that heritage to mobile. It bridges the gap for developers, who can now build next-gen games and apps that will run on any device.”
IBM and NVIDIA plan to collaborate on GPU-accelerated versions of IBM’s wide portfolio of enterprise software applications — taking GPU accelerator technology for the first time into the heart of enterprise-scale data centres.
The collaboration aims to enable IBM customers to more rapidly process, secure and analyse massive volumes of streaming data.
“Harnessing GPU technology to IBM’s enterprise software platforms will bring advanced, in-memory processing to a wider variety of new application areas,” said Sean Poulley, Vice President of Databases and Data Warehousing at IBM. “We are looking at a new generation of higher-performance solutions to help data center customers overcome their most challenging computing problems.”
The NVIDIA Tesla K40 GPU accelerator is arguably the world’s highest performance accelerator ever built. It is capable of delivering extreme performance to a wide range of scientific, engineering, high performance computing (HPC), and enterprise applications.
Providing double the memory and up to 40 percent higher performance than its predecessor, the Tesla K20X GPU accelerator, and 10 times higher performance than the fastest CPU, the Tesla K40 GPU is the world’s first and highest-performance accelerator optimised for big data analytics and large-scale scientific workloads.
Featuring intelligent NVIDIA GPU Boost technology, which converts power headroom into a user-controlled performance boost, the Tesla K40 GPU accelerator enables users to unlock the untapped performance of a broad range of applications.
Visual computing is no longer just about gaming but has now permeated everyday lives, Dr Simon See, Director and Chief Solution Architect of NVIDIA, told a gathering of 450 start-ups, investors and R&D providers in digital media.
He pointed out that GPUs now help power the 3D web, location-based visualisation applications, creative content creation, computer vision, user interfaces, image recognition, HD video processing, and virtual worlds.
In his talk, Simon also discussed the vibrant ecosystem of developers that have adopted the CUDA GPU computing platform, how early stage companies can leverage the GPU for visual and other computing applications, and what global programmes NVIDIA offers to nurture and inspire innovation and business opportunities throughout these ecosystems.
NVIDIA has made further inroads into high performance computing (HPC) with the acquisition of The Portland Group (PGI), a leading independent supplier of compilers and tools.
Founded in 1989, PGI has a long history of innovation in HPC compiler technology for Intel, IBM, Linux, OpenMP, GPGPU, and ARM. Following the acquisition, it will continue to operate under the PGI name and develop OpenACC, CUDA Fortran and CUDA x86 for multicore x86, and GPGPUs. PGI will also continue to serve its customers, including chip makers, research labs and HPC computing centres.
By Edward Lim, Managing Consultant, CIZA Concept
Founded by Rich Ho in Singapore in 2004, Richmanclub Studios is a motion picture production company. Its first official production was the short film, “The Alien Invasion” in 2004, which has been shown around the world. The film was the first Singaporean short film to be nominated for the “Chinese Oscar”, The Golden Horse Awards 2004 for Best International Digital Short Film.
The studio has also won the Special Technical Achievement Award (Hive Film Festival), Audience Favorite (Substation First Take) and the Asia-Pacific wide Gold Award-Digital Art (ACMSIGGRAPH ComGraph).
In 2011, Richmanclub Studios launched two additional departments – Richopus Music to offer music production services and IVI VFX to provide post-production services.
NVIDIA has announced its support for the Microsoft Xbox One game console with its popular NVIDIA PhysX and NVIDIA APEX software development kits (SDKs).
Together, PhysX and APEX provide solutions for collision detection and simulation of rigid bodies, clothing, fluids, particle systems and more, across a wide range of platforms, including desktop PCs, game consoles, and mobile and handheld devices.
NVIDIA PhysX technology is the world’s most pervasive physics solution for designing real-time, real-world effects into interactive entertainment titles. The PhysX development environment gives developers unprecedented control over the look of their final in-game interactivity.
The CUDA Teaching Center Program is designed to support and encourage teaching establishments to include GPU Computing using CUDA C/C++ as part of their course offerings.
Under the program, NVIDIA will donate CUDA enabled GPUs to be installed in teaching lab computers at NTU’s School of Computer Engineering. Students and other members of the university community will have direct access to CUDA enabled systems for hands-on experience of CUDA C/C++ development, debugging and experimentation.
Global visual effects giant Rhythm & Hues (R&H) recently completed the lion’s share of visual effects shots on the acclaimed new film, “Life of Pi,” leveraging NVIDIA GPUs to maximize throughput and accelerate creative workflows. “Life of Pi,” from Academy Award-winning director Ang Lee, tapped legions of R&H artists at offices in Los Angeles, India, Kuala Lumpur, Vancouver, and Taiwan to create several hundred visual effects shots in stereo 3D that included the Bengal tiger, digitally recreated water and skies, Meerkat Island and myriad additional creatures and effects.
R&H is known for its custom development of proprietary visual effects tools, many of which are written specifically for the GPU. One of those tools, dubbed Rampage, was particularly instrumental in achieving the remarkable skies that set the tone in this tale of an Indian zookeeper’s son named Pi, shipwrecked with a Bengal tiger and adrift in the Pacific Ocean.
Seventy more widely used applications have added support for GPU acceleration so far this year, bringing the total number available to researchers, engineers and designers to more than 200, according to NVIDIA.
Three of the newest applications to offer GPU acceleration are:
- ANSYS® Fluent®: ANSYS Fluent enables engineers to develop more aerodynamic cars and planes, which can save millions of dollars in fuel costs, or improve thermal management and reliability of electronic integrated circuit packages. ANSYS Fluent has added a new beta solver with single GPU support to its market-leading NVIDIA® CUDA® applications, including ANSYS Mechanical™.
- MSC® Nastran®: Used by nearly every automotive manufacturer worldwide, MSC Nastran is a GPU-accelerated structural mechanics simulation application that helps optimise noise, vibration and harshness (NVH) performance, which are among the most often directly perceived quality attributes of a vehicle.
- CHARMM: Widely used by scientists to study biological processes at the molecular level, CHARMM’s GPU acceleration enables a more accurate study of key proteins involved in disease, as well as interactions with drug candidates, as a means to develop more effective treatments.
“GPU computing first gained momentum among researchers who could download CUDA to accelerate their own applications for scientific discovery and research,” said Addison Snell, chief executive officer of Intersect360 Research. “We are now in a new era where more commercial software is GPU-optimised, providing accelerated options across the full spectrum of engineering and business computing.”
A partial list of other GPU-accelerating applications shipping or in development includes:
- Computer-aided Engineering: Abaqus/Standard, Agilent ADS & EMPro , ANSYS Mechanical, CST MWS, MSC Nastran, Marc, OpenFOAM solver libraries, RADIOSS™
- Defense & Intelligence: DigitalGlobe Advanced Ortho Series, Exelis (ITT) ENVI, Incogna GIS, Intergraph Motion Video Analyst, MotionDSP Ikena ISR, PCI GEomatics GXL
- Media & Entertainment: Adobe CS6, Autodesk 3ds Max & Maya, Blackmagic DaVinci Resolve, Chaos V-Ray RT, Elemental Server, Telestream Vantage
- Oil & Gas: Acceleware AxRTM, ffA SVI Pro, Headwave Suite, Paradigm Echos RTM, Schlumberger Visage, WesternGeco Omega2 RTM
- Scientific Computing: AMBER, CHARMM, Chroma, FastROCS, GAMESS, GROMACS, GTC, WL-LSMS, MATLAB, MILC, NAMD, QUDA, VASP, VMD
- Weather & Climate Forecasting: COSMO, GEOS-5, HOMME, HYCOM, WRF, NEMO, NIM
A complete list is available at www.nvidia.com/teslaapps.
Most Accessible Parallel Processors
The advent of massively parallel GPU accelerators that are easily programmable in popular high-level languages or using auto-parallelising compilers has given impetus to developers to maximize application performance.
Accelerators give developers a great degree of flexibility to take advantage of dramatic application speedups using familiar languages like C, C++ and Fortran, or using the directives-based OpenACC standard programming model.
Simple extensions to these high-level programming languages enable specifying parallelism using the NVIDIA CUDA parallel computing platform and programming model. Today the CUDA platform is supported by every NVIDIA GPU, resulting in a worldwide installed base of more than 415 million CUDA GPUs.
Learn more about accelerated computing and supported software applications at NVIDIA booth 2217 at SC12, November 12-15.