• Welcome to TechPowerUp Forums, Guest! Please check out our forum guidelines for info related to our community.

NVIDIA Unveils Tesla K80 Dual-Chip Compute Accelerator

btarunr

Editor & Senior Moderator
Staff member
Joined
Oct 9, 2007
Messages
46,589 (7.66/day)
Location
Hyderabad, India
System Name RBMK-1000
Processor AMD Ryzen 7 5700G
Motherboard ASUS ROG Strix B450-E Gaming
Cooling DeepCool Gammax L240 V2
Memory 2x 8GB G.Skill Sniper X
Video Card(s) Palit GeForce RTX 2080 SUPER GameRock
Storage Western Digital Black NVMe 512GB
Display(s) BenQ 1440p 60 Hz 27-inch
Case Corsair Carbide 100R
Audio Device(s) ASUS SupremeFX S1220A
Power Supply Cooler Master MWE Gold 650W
Mouse ASUS ROG Strix Impact
Keyboard Gamdias Hermes E2
Software Windows 11 Pro
NVIDIA today unveiled a new addition to the NVIDIA Tesla Accelerated Computing Platform: the Tesla K80 dual-GPU accelerator, the world's highest performance accelerator designed for a wide range of machine learning, data analytics, scientific, and high performance computing (HPC) applications.

The Tesla K80 dual-GPU is the new flagship offering of the Tesla Accelerated Computing Platform, the leading platform for accelerating data analytics and scientific computing. It combines the world's fastest GPU accelerators, the widely used CUDA parallel computing model, and a comprehensive ecosystem of software developers, software vendors, and datacenter system OEMs.



The Tesla K80 dual-GPU accelerator delivers nearly two times higher performance and double the memory bandwidth of its predecessor, the Tesla K40 GPU accelerator. With ten times higher performance than today's fastest CPU, it outperforms CPUs and competing accelerators on hundreds of complex analytics and large, computationally intensive scientific computing applications.

Users can unlock the untapped performance of a broad range of applications with the accelerator's enhanced version of NVIDIA GPU Boost technology (PDF), which dynamically converts power headroom into the optimal performance boost for each individual application.

Industry-Leading Performance for Science, Data Analytics, Machine Learning
The Tesla K80 dual-GPU accelerator was designed with the most difficult computational challenges in mind, ranging from astrophysics, genomics and quantum chemistry to data analytics. It is also optimized for advanced deep learning tasks, one of the fastest growing segments of the machine learning field.

"NVIDIA GPUs have become the de facto computing platform for the deep learning community," said Yann LeCun, director of AI Research at Facebook, and Silver Professor of Computer Science & Neural Science at New York University. "Because the accuracy of deep learning systems improves as the models and datasets get larger, we always look for the fastest hardware we can find. The Tesla K80 accelerator, with its dual-GPU architecture and large memory, gives us more teraflops and more GB than ever before from a single server, allowing us to make faster progress in deep learning."

The Tesla K80 delivers up to 8.74 teraflops single-precision and up to 2.91 teraflops double-precision peak floating point performance, and10 times higher performance than today's fastest CPUs on leading science and engineering applications, such as AMBER, GROMACS, Quantum Espresso and LSMS.

"The Tesla K80 dual-GPU accelerators are up to 10 times faster than CPUs when enabling scientific breakthroughs in some of our key applications, and provide a low energy footprint," said Wolfgang Nagel, director of the Center for Information Services and HPC at Technische Universität Dresden in Germany. "Our researchers use the available GPU resources on the Taurus supercomputer extensively to enable a more refined cancer therapy, understand cells by watching them live, and study asteroids as part of ESA's Rosetta mission."

Key features of the Tesla K80 dual-GPU accelerator include:
  • Two GPUs per board - Doubles throughput of applications designed to take advantage of multiple GPUs.
  • 24GB of ultra-fast GDDR5 memory - 12GB of memory per GPU, 2x more memory than Tesla K40 GPU, allows users to process 2x larger datasets.
  • 480GB/s memory bandwidth - Increased data throughput allows data scientists to crunch though petabytes of information in half the time compared to the Tesla K10 accelerator. Optimized for energy exploration, video and image processing, and data analytics applications.
  • 4,992 CUDA parallel processing cores - Accelerates applications by up to 10x compared to using a CPU alone.
  • Dynamic NVIDIA GPU Boost Technology - Dynamically scales GPU clocks based on the characteristics of individual applications for maximum performance.
  • Dynamic Parallelism - Enables GPU threads to dynamically spawn new threads, enabling users to quickly and easily crunch through adaptive and dynamic data structures.
The Tesla K80 accelerates the broadest range of scientific, engineering, commercial and enterprise HPC and data center applications -- more than 280 in all. The complete catalog of GPU-accelerated applications (PDF) is available as a free download.

More information about the Tesla K80 dual-GPU accelerator is available at NVIDIA booth 1727 at SC14, Nov. 17-20, and on the NVIDIA high performance computing website.

Users can also try the Tesla K80 dual-GPU accelerator for free on remotely hosted clusters. Visit the GPU Test Drive website for more information.

Availability
Shipping today, the NVIDIA Tesla K80 dual-GPU accelerator will be available from a variety of server manufacturers, including ASUS, Bull, Cirrascale, Cray, Dell, Gigabyte, HP, Inspur, Penguin, Quanta, Sugon, Supermicro and Tyan, as well as from NVIDIA reseller partners.

View at TechPowerUp Main Site
 
Joined
Apr 29, 2014
Messages
4,204 (1.14/day)
Location
Texas
System Name SnowFire / The Reinforcer
Processor i7 10700K 5.1ghz (24/7) / 2x Xeon E52650v2
Motherboard Asus Strix Z490 / Dell Dual Socket (R720)
Cooling RX 360mm + 140mm Custom Loop / Dell Stock
Memory Corsair RGB 16gb DDR4 3000 CL 16 / DDR3 128gb 16 x 8gb
Video Card(s) GTX Titan XP (2025mhz) / Asus GTX 950 (No Power Connector)
Storage Samsung 970 1tb NVME and 2tb HDD x4 RAID 5 / 300gb x8 RAID 5
Display(s) Acer XG270HU, Samsung G7 Odyssey (1440p 240hz)
Case Thermaltake Cube / Dell Poweredge R720 Rack Mount Case
Audio Device(s) Realtec ALC1150 (On board)
Power Supply Rosewill Lightning 1300Watt / Dell Stock 750 / Brick
Mouse Logitech G5
Keyboard Logitech G19S
Software Windows 11 Pro / Windows Server 2016
Wow that is one BEAUTIFUL Tesla card. Man 12gb per GPU (Though 24gb for professional work!!!).

I love the design of these Tesla cards, just fantastic looking crafted and refined work horses!
 

64K

Joined
Mar 13, 2014
Messages
6,260 (1.68/day)
Processor i7 7700k
Motherboard MSI Z270 SLI Plus
Cooling CM Hyper 212 EVO
Memory 2 x 8 GB Corsair Vengeance
Video Card(s) MSI RTX 2070 Super
Storage Samsung 850 EVO 250 GB and WD Black 4TB
Display(s) Dell 27 inch 1440p 144 Hz
Case Corsair Obsidian 750D Airflow Edition
Audio Device(s) Onboard
Power Supply EVGA SuperNova 850 W Gold
Mouse Logitech G502
Keyboard Logitech G105
Software Windows 10
That really is a nice looking card and the performance is crazy fast.




Source: videocardz.com
 
Last edited:
Joined
Oct 17, 2012
Messages
9,781 (2.30/day)
Location
Massachusetts
System Name Americas cure is the death of Social Justice & Political Correctness
Processor i7-11700K
Motherboard Asrock Z590 Extreme wifi 6E
Cooling Noctua NH-U12A
Memory 32GB Corsair RGB fancy boi 5000
Video Card(s) RTX 3090 Reference
Storage Samsung 970 Evo 1Tb + Samsung 970 Evo 500Gb
Display(s) Dell - 27" LED QHD G-SYNC x2
Case Fractal Design Meshify-C
Audio Device(s) on board
Power Supply Seasonic Focus+ Gold 1000 Watt
Mouse Logitech G502 spectrum
Keyboard AZIO MGK-1 RGB (Kaith Blue)
Software Win 10 Professional 64 bit
Benchmark Scores the MLGeesiest
I use one of these, as a booster for My Microsoft Security Essentials Scans....... ;).
 
Joined
Oct 10, 2009
Messages
868 (0.16/day)
Location
London, UK
System Name The one under the desk / Media Centre
Processor Xeon X3730@3.6GHZ / Phenom II X4 805E
Motherboard Gigabyte P55M-UD4 / Asus Crosshair III
Cooling Corsair H70 + 2*PWM fan / Arctic Alpine 11
Memory 16GB DRR3-1333 9-9-9-27 / 4GB Crucial DDR3-1333
Video Card(s) Asus DirectCU GTX 680 / Gigabyte 560TI
Storage Kingston V200 128GB, WD6400AAKS, 1TB Seagate 7.2kRPM SSHD / Kingston V200 128GB
Display(s) Samsung 2343BW + Dell Ultrasharp 1600*1200 / 32" TV
Case C'M' Silencio 550 / Some ancient SilverStone brushed aluminium media centre
Audio Device(s) No.
Power Supply Thermaltake Toughpower XT 675W / EVGA 430W
Mouse Mionix Naos 3200 / Generic PS2
Keyboard Roccat Ryos TKL Pro / Evoluent Mouse Friendly Keyboard (Logitech OEM)
Software Windows 7 Ult x64
Benchmark Scores Nah.


Source: videocardz.com

This graph is really misleading - of course absolute difference grows over time. Absolute difference would grow if you went from [1 CPU vs. 1 GPU] to [2 CPUs vs. 2 GPUs]. Has the relative difference grown, showing that GPUs are genuinely extending their lead? It's hard to tell based on these graphs.
 
Joined
Sep 7, 2011
Messages
2,785 (0.60/day)
Location
New Zealand
System Name MoneySink
Processor 2600K @ 4.8
Motherboard P8Z77-V
Cooling AC NexXxos XT45 360, RayStorm, D5T+XSPC tank, Tygon R-3603, Bitspower
Memory 16GB Crucial Ballistix DDR3-1600C8
Video Card(s) GTX 780 SLI (EVGA SC ACX + Giga GHz Ed.)
Storage Kingston HyperX SSD (128) OS, WD RE4 (1TB), RE2 (1TB), Cav. Black (2 x 500GB), Red (4TB)
Display(s) Achieva Shimian QH270-IPSMS (2560x1440) S-IPS
Case NZXT Switch 810
Audio Device(s) onboard Realtek yawn edition
Power Supply Seasonic X-1050
Software Win8.1 Pro
Benchmark Scores 3.5 litres of Pale Ale in 18 minutes.
Interesting evolution. Tesla used to a by-product of consumer graphics, then GK 110 launched first as a professional series, now GK 210 is entirely a pro SKU.
 
Joined
Dec 16, 2010
Messages
1,662 (0.34/day)
Location
State College, PA, US
System Name My Surround PC
Processor AMD Ryzen 9 7950X3D
Motherboard ASUS STRIX X670E-F
Cooling Swiftech MCP35X / EK Quantum CPU / Alphacool GPU / XSPC 480mm w/ Corsair Fans
Memory 96GB (2 x 48 GB) G.Skill DDR5-6000 CL30
Video Card(s) MSI NVIDIA GeForce RTX 4090 Suprim X 24GB
Storage WD SN850 2TB, 2 x 512GB Samsung PM981a, 4 x 4TB HGST NAS HDD for Windows Storage Spaces
Display(s) 2 x Viotek GFI27QXA 27" 4K 120Hz + LG UH850 4K 60Hz + HMD
Case NZXT Source 530
Audio Device(s) Sony MDR-7506 / Logitech Z-5500 5.1
Power Supply Corsair RM1000x 1 kW
Mouse Patriot Viper V560
Keyboard Corsair K100
VR HMD HP Reverb G2
Software Windows 11 Pro x64
Benchmark Scores Mellanox ConnectX-3 10 Gb/s Fiber Network Card
Would the lack of a GeForce product be the reason for having only 2496 shaders per chip?

I thought that Nvidia designated that chips with defects get sold as GeForce products. Since GK210 doesn't seem like it will ever make it to a GeForce card and since yields will never be great on a ~600mm^2 chip then they would have to use harvested silicon or face very low yields/high costs.
 
Joined
Sep 7, 2011
Messages
2,785 (0.60/day)
Location
New Zealand
System Name MoneySink
Processor 2600K @ 4.8
Motherboard P8Z77-V
Cooling AC NexXxos XT45 360, RayStorm, D5T+XSPC tank, Tygon R-3603, Bitspower
Memory 16GB Crucial Ballistix DDR3-1600C8
Video Card(s) GTX 780 SLI (EVGA SC ACX + Giga GHz Ed.)
Storage Kingston HyperX SSD (128) OS, WD RE4 (1TB), RE2 (1TB), Cav. Black (2 x 500GB), Red (4TB)
Display(s) Achieva Shimian QH270-IPSMS (2560x1440) S-IPS
Case NZXT Switch 810
Audio Device(s) onboard Realtek yawn edition
Power Supply Seasonic X-1050
Software Win8.1 Pro
Benchmark Scores 3.5 litres of Pale Ale in 18 minutes.
Would the lack of a GeForce product be the reason for having only 2496 shaders per chip?
I thought that Nvidia designated that chips with defects get sold as GeForce products. Since GK210 doesn't seem like it will ever make it to a GeForce card and since yields will never be great on a ~600mm^2 chip then they would have to use harvested silicon or face very low yields/high costs.
2496 cores/ 13SMX is actually the same core/SM count as the original Tesla K20. The reduced count could be to fit within the 300w power budget as is the reduced memory speed (5GHz instead of 5.2). 24GB of GDDR5 would suck up a sizeable portion of the board power. The other possibility could be yields, but I'm picking that two of the missing numbers in the sequence (K20, K40, K50, K60, K70, K80) might equate to the two higher bin parts (2688 core/14SMX, 2880 core/15SMX). Nvidia might want to decrease inventory levels of the K40 before it looks at a replacement SKU. The dual card launching first makes some sense because it isn't replacing anything, its performance makes it a convincing competitor to Xeon Phi, and also conveniently showcases NVLink.
 

Aquinus

Resident Wat-man
Joined
Jan 28, 2012
Messages
13,148 (2.91/day)
Location
Concord, NH, USA
System Name Apollo
Processor Intel Core i9 9880H
Motherboard Some proprietary Apple thing.
Memory 64GB DDR4-2667
Video Card(s) AMD Radeon Pro 5600M, 8GB HBM2
Storage 1TB Apple NVMe, 4TB External
Display(s) Laptop @ 3072x1920 + 2x LG 5k Ultrafine TB3 displays
Case MacBook Pro (16", 2019)
Audio Device(s) AirPods Pro, Sennheiser HD 380s w/ FIIO Alpen 2, or Logitech 2.1 Speakers
Power Supply 96w Power Adapter
Mouse Logitech MX Master 3
Keyboard Logitech G915, GL Clicky
Software MacOS 12.1
Too bad that like most OpenCL devices, the Tesla card needs to be used for very specific situations. Not all workloads can benefit from a super wide execution engine. So all in all, unless you're working on big data and most of your code aren't serial workloads, this doesn't mean a whole much.
 
Joined
Sep 7, 2011
Messages
2,785 (0.60/day)
Location
New Zealand
System Name MoneySink
Processor 2600K @ 4.8
Motherboard P8Z77-V
Cooling AC NexXxos XT45 360, RayStorm, D5T+XSPC tank, Tygon R-3603, Bitspower
Memory 16GB Crucial Ballistix DDR3-1600C8
Video Card(s) GTX 780 SLI (EVGA SC ACX + Giga GHz Ed.)
Storage Kingston HyperX SSD (128) OS, WD RE4 (1TB), RE2 (1TB), Cav. Black (2 x 500GB), Red (4TB)
Display(s) Achieva Shimian QH270-IPSMS (2560x1440) S-IPS
Case NZXT Switch 810
Audio Device(s) onboard Realtek yawn edition
Power Supply Seasonic X-1050
Software Win8.1 Pro
Benchmark Scores 3.5 litres of Pale Ale in 18 minutes.
Too bad that like most OpenCL devices, the Tesla card needs to be used for very specific situations. Not all workloads can benefit from a super wide execution engine. So all in all, unless you're working on big data and most of your code aren't serial workloads, this doesn't mean a whole much.
Isn't that the point?
This seems aimed squarely at Nvidia's contribution to OpenPOWER (NVLink included). Look at the launch partners (Cray, Dell, HP, Quanta), the timing (both at SC14) WRT to Knights Landing/Knights Hill and POWER9, and who owns the non-3D big data space (IBM). SC14 is basically an exercise in muscle flexing for three consortiums.
 
Top