What’s a three-letter acronym for a “video-handling chip”? A GPU, of course. Who knew, though, that these parallel processing powerhouses could have a way with words, too. Following a long string of victories for computers in other games — chess in 1997, go in 2016 and Texas hold’em poker in 2019 — a GPU-powered AI Read article >
What’s a three-letter acronym for a “video-handling chip”? A GPU, of course. Who knew, though, that these parallel processing powerhouses could have a way with words, too.
Following a long string of victories for computers in other games — chess in 1997, go in 2016 and Texas hold’em poker in 2019 — a GPU-powered AI has beaten some of the world’s most competitive word nerds at the crossword puzzles that are a staple of every Sunday paper.
Dr.Fill, the crossword puzzle-playing AI created by Matt Ginsberg — a serial entrepreneur, pioneering AI researcher and former research professor — scored higher than any humans last month at the American Crossword Puzzle Tournament.
Dr.Fill’s performance against more than 1,300 crossword enthusiasts comes after a decade of playing alongside humans through the annual tournament.
Such games, played competitively, test the limits of how computers think and better understand how people do, Ginsberg explains. “Games are an amazing environment,” he says.
Dr.Fill’s edge? A sophisticated neural network developed by UC Berkeley’s Natural Language Processing team — trained in just days on an NVIDIA DGX-1 system and deployed on a PC equipped with a pair of NVIDIA GeForce RTX 2080 Ti GPUs — that snapped right into the system Ginsberg had been refining for years.
“Crossword fills require you to make these creative multi-hop lateral connections with language,” says Professor Dan Klein, who leads the Natural Language Processing team. “I thought it would be a good test to see how the technology we’ve created in this field would handle that kind of creative language use.”
Given that unstructured nature, it’s amazing that a computer can compete at all. And to be sure, Dr.Fill still isn’t necessarily the best, and that’s not only because the American Crossword Puzzle Tournament’s official championship is reserved only for humans.
The contest’s organizer, New York Times Puzzle Editor Will Shortz, pointed out that Dr.Fill’s biggest advantage is speed: it can fill in answers in an instant that humans have to type out. Judged solely by accuracy, however, Dr.Fill still isn’t the best, making three errors during the contest, worse than several human contestants.
Nevertheless, Dr.Fill’s performance in a challenge that, unlike more structured games such as chess or go, rely so heavily on real-world knowledge and wordplay is remarkable, Shortz concedes.
“It’s just amazing they have programmed a computer to solve crosswords — especially some of the tricky hard ones,” Shortz said.
A Way with Words
Ginsberg, who holds a Ph.D. in mathematics from the University of Oxford and has 100 technical papers, 14 patents and multiple books to his name, has been a crossword fan since he attended college 45 years ago.
But his obsession took off when he entered a tournament more than a decade ago and didn’t win.
“‘The other competitors were so much better than I was, and it annoyed me, so I thought ‘Well, I should write a program,’ so I started Dr.Fill,” Ginsberg says.
Organized by Shortz, the American Crossword Tournament is packed with people who know their way around words.
Dr.Fill made its debut at the competition in 2012. Despite high expectations, Dr.Fill only managed to place 141st out of 600 contestants. Dr.Fill never managed a top 10 finish until this year.
In part, that’s because crosswords didn’t attract the kind of richly funded efforts that took on — and eventually beat — the best humans at chess and go.
It’s also partly because crossword puzzles are unique. “In go and chess and checkers, the rules are very clear,” Ginsberg says. “Crosswords are very interesting.”
Crossword puzzles often rely on cryptic clues that require deep cultural knowledge and an extensive vocabulary, as well as the ability to find answers that best slide into each puzzle’s overlapping rows and columns.
“It’s a messy thing,” Shortz said. “It’s not purely logical like chess or even like Scrabble, where you have a word list and every word is worth so many points.”
A Winning Combination
The game-changer? Help from the Natural Language Processing team. Inspired by his efforts, the team reached out to Ginsberg a month before the competition began.
It proved to be a triumphant combination.
The Berkeley team focused on understanding each puzzle’s often gnomic clues and finding potential answers. Klein’s team of three graduate students and two undergrads took the more than 6 million examples of crossword clues and answers that Ginsberg had collected and poured them into a sophisticated neural network.
Ginsberg’s software, refined over many years, then handled the task of ranking all the answers that fit the confines of each puzzle’s grid and fitting them in with overlapping letters from other answers — a classic constraint satisfaction problem.
While their systems relied on very different techniques, they both spoke the common language of probabilities. As a result, they snapped together almost perfectly.
“We quickly realized that we had very complementary pieces of the puzzle,” Klein said.
Together, their models parallel some of the ways people think, Klein says. Humans make decisions by either remembering what worked in the past or using a model to simulate of what might work in the future.
“I get excited when I see systems that do some of both,” Klein said.
The result of combining both approaches: Dr.Fill played almost perfectly.
The AI made just three errors during the tournament. Its biggest edge, however, was speed. It dispatched most of the competition’s puzzles in under a minute.
AI Supremacy Anything But Assured
But since, unlike chess or go, crossword puzzles are ever-changing, another such showing isn’t guaranteed.
“It’s very likely that the constructors will throw some curveballs,” Shortz said.
Ginsberg says he’s already working to improve Dr.Fill. “We’ll see who makes more progress.”
The result may be out to be even more engaging crossword puzzles than ever.
“It turns out that the things that are going to stump a computer are really creative,” Klein said.
It will help piece together a 3D map of the universe, probe subatomic interactions for green energy sources and much more.
Perlmutter, officially dedicated today at the National Energy Research Scientific Computing Center (NERSC), is a supercomputer that will deliver nearly four exaflops of AI performance for more than 7,000 researchers.
That makes Perlmutter the fastest system on the planet on the 16- and 32-bit mixed-precision math AI uses. And that performance doesn’t even include a second phase coming later this year to the system based at Lawrence Berkeley National Lab.
More than two dozen applications are getting ready to be among the first to ride the 6,159 NVIDIA A100 Tensor Core GPUs in Perlmutter, the largest A100-powered system in the world. They aim to advance science in astrophysics, climate science and more.
A 3D Map of the Universe
In one project, the supercomputer will help assemble the largest 3D map of the visible universe to date. It will process data from the Dark Energy Spectroscopic Instrument (DESI), a kind of cosmic camera that can capture as many as 5,000 galaxies in a single exposure.
Researchers need the speed of Perlmutter’s GPUs to capture dozens of exposures from one night to know where to point DESI the next night. Preparing a year’s worth of the data for publication would take weeks or months on prior systems, but Perlmutter should help them accomplish the task in as little as a few days.
“I’m really happy with the 20x speedups we’ve gotten on GPUs in our preparatory work,” said Rollin Thomas, a data architect at NERSC who’s helping researchers get their code ready for Perlmutter.
Perlmutter’s Persistence Pays Off
DESI’s map aims to shed light on dark energy, the mysterious physics behind the accelerating expansion of the universe. Dark energy was largely discovered through the 2011 Nobel Prize-winning work of Saul Perlmutter, a still-active astrophysicist at Berkeley Lab who will help dedicate the new supercomputer named for him.
“To me, Saul is an example of what people can do with the right combination of insatiable curiosity and a commitment to optimism,” said Thomas, who worked with Perlmutter on projects following up the Nobel-winning discovery.
Supercomputer Blends AI, HPC
A similar spirit fuels many projects that will run on NERSC’s new supercomputer. For example, work in materials science aims to discover atomic interactions that could point the way to better batteries and biofuels.
Traditional supercomputers can barely handle the math required to generate simulations of a few atoms over a few nanoseconds with programs such as Quantum Espresso. But by combining their highly accurate simulations with machine learning, scientists can study more atoms over longer stretches of time.
“In the past it was impossible to do fully atomistic simulations of big systems like battery interfaces, but now scientists plan to use Perlmutter to do just that,” said Brandon Cook, an applications performance specialist at NERSC who’s helping researchers launch such projects.
That’s where Tensor Cores in the A100 play a unique role. They accelerate both the double-precision floating point math for simulations and the mixed-precision calculations required for deep learning.
Similar work won NERSC recognition in November as a Gordon Bell finalist for its BerkeleyGW program using NVIDIA V100 GPUs. The extra muscle of the A100 promises to take such efforts to a new level, said Jack Deslippe, who led the project and oversees application performance at NERSC.
Software Helps Perlmutter Sing
Software is a strategic component of Perlmutter, too, said Deslippe, noting support for OpenMP and other popular programming models in the NVIDIA HPC SDK the system uses.
Separately, RAPIDS, open-source code for data science on GPUs, will speed the work of NERSC’s growing team of Python programmers. It proved its value in a project that analyzed all the network traffic on NERSC’s Cori supercomputer nearly 600x faster than prior efforts on CPUs.
“That convinced us RAPIDS will play a major part in accelerating scientific discovery through data,” said Thomas.
Coping with COVID’s Challenges
Despite the pandemic, Perlmutter is on schedule. But the team had to rethink critical steps like how it ran hackathons for researchers working from home on code for the system’s exascale-class applications.
Meanwhile, engineers from Hewlett Packard Enterprise helped assemble phase 1 of the system, collaborating with NERSC staff who upgraded their facility to accommodate the new system. “We greatly appreciate the work of those people onsite bringing the system up, especially under all the special COVID protocols,” said Thomas.
At the virtual launch event, NVIDIA CEO Jensen Huang congratulated the Berkeley Lab crew on its plans to advance science with the supercomputer.
“Perlmutter’s ability to fuse AI and high performance computing will lead to breakthroughs in a broad range of fields from materials science and quantum physics to climate projections, biological research and more,” Huang said.
On Time for AI Supercomputing
The virtual ribbon cutting today represents a very real milestone.
“AI for science is a growth area at the U.S. Department of Energy, where proof of concepts are moving into production use cases in areas like particle physics, materials science and bioenergy,” said Wahid Bhimji, acting lead for NERSC’s data and analytics services group.
“People are exploring larger and larger neural-network models and there’s a demand for access to more powerful resources, so Perlmutter with its A100 GPUs, all-flash file system and streaming data capabilities is well timed to meet this need for AI,” he added.
It will help piece together a 3D map of the universe, probe subatomic interactions for green energy sources and much more. Perlmutter, officially dedicated today at the National Energy Research Scientific Computing Center (NERSC), is a supercomputer that will deliver nearly four exaflops of AI performance for more than 7,000 researchers. That makes Perlmutter the Read article >
— New system will enable next-gen research on clean energy, climate and more for the National Energy Research Scientific Computing Center —
SANTA CLARA, Calif., May 27, 2021 (GLOBE NEWSWIRE) -- AMD (NASDAQ: AMD) today joined the National Energy Research Scientific Computing Center (NERSC), Lawrence Berkeley National Laboratory (Berkeley Lab) and others in unveiling the new Perlmutter supercomputer powered by AMD EPYC™ 7003 Series processors.
The new supercomputer at Berkeley Lab will provide four times the computational power currently available at NERSC, making it among the fastest supercomputers in the world for scientific simulation, data analysis and artificial intelligence (AI). In development since 2019, the new system takes advantage of the industry-leading HPC workload performance1 offered by the new 3rd Gen AMD EPYC processors to facilitate faster and advanced research in climate, clean energy, semiconductors, microelectronics and quantum information science.
“AMD is extremely proud to work with our strategic partners to push the boundaries of HPC in areas including scientific and environmental research, medical advancements and artificial intelligence,” said Forrest Norrod, senior vice president and general manager, Data Center and Embedded Solutions Business Group. “The new Perlmutter supercomputer from NERSC, will drive the next wave of critical discoveries that help to solve the world’s biggest challenges.”
“Our work with key partners like AMD enables us to significantly increase our computing power and broaden our spectrum of scientific capabilities,” said NERSC Director Sudip Dosanjh. “Perlmutter will enable a larger range of applications than previous NERSC systems and is the first NERSC supercomputer designed from the start to meet the needs of both simulation and data analysis."
Perlmutter, named in honor of Nobel Prize-winning astrophysicist Saul Perlmutter, is being delivered in two phases. Phase 1 is now being deployed and features 1,536 nodes, each with one AMD EPYC 7763 processor and four NVIDIA NVlink-connected A100 Tensor Core GPUs. Phase 1 also includes a 35 PB all-flash Lustre file system that will provide very high-bandwidth storage. Expected later this year, phase 2 will add another 3,072 CPU-only nodes, each with two AMD EPYC 7763 processors and 512 GB of memory per node.
About AMD For more than 50 years AMD has driven innovation in high-performance computing, graphics and visualization technologies ― the building blocks for gaming, immersive platforms and the datacenter. Hundreds of millions of consumers, leading Fortune 500 businesses and cutting-edge scientific research facilities around the world rely on AMD technology daily to improve how they live, work and play. AMD employees around the world are focused on building great products that push the boundaries of what is possible. For more information about how AMD is enabling today and inspiring tomorrow, visit the AMD (NASDAQ: AMD) website, blog, Facebook and Twitter pages.
AMD, the AMD Arrow logo and EPYC, are trademarks of Advanced Micro Devices, Inc. Other names are for informational purposes only and may be trademarks of their respective owners.
Here’s a chance to become a marvel at marbles: the Marbles RTX playable sample is now available from the NVIDIA Omniverse launcher. Marbles RTX is a physics-based mini-game level where a player controls a marble around a scene full of obstacles. The sample, which already has over 8,000 downloads, displays real-time physics with dynamic lighting Read article >
Here’s a chance to become a marvel at marbles: the Marbles RTX playable sample is now available from the NVIDIA Omniverse launcher.
Marbles RTX is a physics-based mini-game level where a player controls a marble around a scene full of obstacles. The sample, which already has over 8,000 downloads, displays real-time physics with dynamic lighting and stunning, physically based materials.
The technology demo showcases NVIDIA Omniverse’s powerful suite of graphics, AI and simulation technologies. GeForce RTX gaming and NVIDIA RTX enthusiasts can download Marbles RTX and experience Omniverse’s advanced capabilities in real-time ray- and path-traced rendering, Deep Learning Super Sampling (DLSS) and complex physics simulation.
First previewed at GTC 2020, the Marbles RTX tech demo simulates the dynamic world in real time, without any precomputation or baking. It highlights NVIDIA’s advanced rendering and physics with exceptionally high-quality 3D content created from scratch.
The final Marbles RTX tech demo completed in Omniverse resulted in over 500GB of texture data, 165 unique assets that were modeled and textured by hand, more than 5,000 meshes and about 100 million polygons.
During the GeForce RTX 30 Series launch event in September, NVIDIA unveiled a more challenging take on the demo with the release of Marbles at Night RTX. This presented a night scene that contained hundreds of dynamic, animated lights. Based on NVIDIA Research, Marbles at Night RTX showcased how the power and beauty of RTX-enabled real-time ray tracing allows artists to render dynamic direct lighting and shadows from millions of area lights in real time.
The combination of physically based MDL materials and real-time, referenced path tracing in Omniverse brings high-quality details to the Marbles scene, enabling players to feel like they’re looking at real-world objects. The Omniverse RTX Renderer calculates reflections, refraction and global illumination accurately while the denoiser easily manages all the complex geometry across the entire scene.
NVIDIA PhysX 5 and Flow simulate the interaction of rigid-body objects and fluids in the scene in real time, and NVIDIA DLSS enhances the details in the image with powerful AI, allowing users to focus GPU resources on accuracy and fidelity. All these elements combined provide a unique look and feel in CGI that users typically can’t get from real-time games.
At GTC 2021, the artists behind Marbles RTX hosted an exclusive deep dive session detailing the creative and development process. Learn more about the Making of Marbles by watching the GTC session on demand, which is available now and free to access.
GFN Thursday comes roaring in with 22 games and support for three DLCs joining the GeForce NOW library this week. Among the 22 new releases are five day-and-date game launches: Biomutant, Maneater, King of Seas, Imagine Earth and Warhammer Age of Sigmar: Storm Ground. DLC, Without the Download GeForce NOW ensures your favorite games are Read article >
GFN Thursday comes roaring in with 22 games and support for three DLCs joining the GeForce NOW library this week.
Among the 22 new releases are five day-and-date game launches: Biomutant, Maneater, King of Seas, Imagine Earth and Warhammer Age of Sigmar: Storm Ground.
DLC, Without the Download
GeForce NOW ensures your favorite games are automatically up to date, avoiding game updates and patches. Simply log in, click PLAY and enjoy an optimal cloud gaming experience.
This includes supporting the latest expansions and other downloadable content — without any local downloads.
Three great games are getting new DLC, and they’re streaming on GeForce NOW.
Hunt Showdown — The Committed DLC contains one Legendary Hunter (Monroe), a Legendary knife (Pane) and a Legendary Romero 77 (Lock and Key). It’s available on Steam, so members can start hunting now.
Isle of Siptah, the massive expansion to the open world survival game Conan Exiles, is exiting early access and releasing on Steam today. It features a vast new island to explore, huge and vile new creatures to slay, new building sets and a host of new features. Gamers have 40 new NPC camps and points of interest to explore, three new factions of NPCs, new ways of acquiring thralls and much more.
Announced last month, Iron Harvest – Operation Eagle, the new expansion to the critically acclaimed world of Iron Harvest set in the alternate reality of 1920, is available on Steam and streaming with GeForce NOW. Guide the new faction through seven new single-player missions, while learning how to use the game’s new Aircraft units across all of the game’s playable factions, including Polania, Saxony and Rusviet.
Newest Additions of the Week
GFN Thursday wouldn’t be complete without new games. The library evolved this week, but didn’t chew you up, with five day-and-date releases, including the launch of Biomutant, from Experiment 101 and THQ Nordic.
Sven G is back with another tale of using a Raspberry Pi in his garage:
OpenBSD lets one control the GPIO pins on a Raspberry Pi. Controlling
a garage door is simple: connect the GPIO output pin to one side of a
relay's coil, connect the 5 volt output of the Pi to the other side of
the relay's coil, and connect wires from your garage's wall console to
the relay's common and "normally closed" ports. Running the program below
opens or closes the door. Since the Pi will be connected to the garage
wall console, you'll want to enable sshd. I've named my Pi "garage"
and my program "og," so I can open the door remotely with