The document discusses NVIDIA data center GPUs such as the A100, A30, A40, and A10 and their performance capabilities. It provides examples of GPU accelerated application performance showing simulations in Simulia CST Studio, Altair CFD, and Rocky DEM achieving excellent speedups on GPUs. It also discusses Paraview visualization being accelerated with NVIDIA OptiX ray tracing, further sped up using RT cores. Looking ahead, the document outlines NVIDIA Grace CPUs which are designed to improve memory bandwidth between CPUs and GPUs for giant AI and HPC models.
NVIDIA GPUs Power HPC & AI Workloads in Cloud with Univainside-BigData.com
In this deck from the Univa Breakfast Briefing at ISC 2018, Duncan Poole from NVIDIA describes how the company is accelerating HPC in the Cloud.
Learn more: https://www.nvidia.com/en-us/data-center/dgx-systems/
and
http://univa.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Today’s groundbreaking scientific discoveries are taking place in HPC data centers. Using containers, researchers and scientists gain the flexibility to run HPC application containers on NVIDIA Volta-powered systems including Quadro-powered workstations, NVIDIA DGX Systems, and HPC clusters.
NVIDIA GPUs Power HPC & AI Workloads in Cloud with Univainside-BigData.com
In this deck from the Univa Breakfast Briefing at ISC 2018, Duncan Poole from NVIDIA describes how the company is accelerating HPC in the Cloud.
Learn more: https://www.nvidia.com/en-us/data-center/dgx-systems/
and
http://univa.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Today’s groundbreaking scientific discoveries are taking place in HPC data centers. Using containers, researchers and scientists gain the flexibility to run HPC application containers on NVIDIA Volta-powered systems including Quadro-powered workstations, NVIDIA DGX Systems, and HPC clusters.
At the 2017 GPU Technology Conference in Silicon Valley, NVIDIA CEO Jensen Huang introduced a lineup of new Volta-based AI supercomputers including a powerful new version of our DGX-1 deep learning appliance; announced the Isaac robot-training simulator; unveiled the NVIDIA GPU Cloud platform, giving developers access to the latest, optimized deep learning frameworks; and unveiled a partnership with Toyota to help build a new generation of autonomous vehicles.
Jetson AGX Xavier and the New Era of Autonomous MachinesDustin Franklin
Deep-dive on NVIDIA Jetson AGX Xavier, designed to help you deploy advanced AI onboard robots, drones, and other autonomous machines. View the webinar here: https://bit.ly/2BWVWv1
Nvidia Deep Learning Solutions - Alex SabatierSri Ambati
Alex Sabatier from Nvidia talks about the future of Deep Learning from an chipmaker perspective
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Implementing AI: High Performance Architectures: A Universal Accelerated Comp...KTN
The Implementing AI: High Performance Architectures webinar, hosted by KTN and eFutures, was the fourth event in the Implementing AI webinar series.
The focus of the webinar was the impact of processing AI data on data centres - particularly from the technology perspective. Timothy Lanfear, Director of Solution Architecture and Engineering EMEA, NVIDIA, presented on a Universal Accelerated Computing Platform.
In this deck from the UK HPC Conference, Gunter Roeth from NVIDIA presents: Hardware & Software Platforms for HPC, AI and ML.
"Data is driving the transformation of industries around the world and a new generation of AI applications are effectively becoming programs that write software, powered by data, vs by computer programmers. Today, NVIDIA’s tensor core GPU sits at the core of most AI, ML and HPC applications, and NVIDIA software surrounds every level of such a modern application, from CUDA and libraries like cuDNN and NCCL embedded in every deep learning framework and optimized and delivered via the NVIDIA GPU Cloud to reference architectures designed to streamline the deployment of large scale infrastructures."
Watch the video: https://wp.me/p3RLHQ-l2Y
Learn more: http://nvidia.com
and
http://hpcadvisorycouncil.com/events/2019/uk-conference/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
NF5288M5(AGX-2)
NF5288M5 is a 2U two-sockets rack server consists 8 NVLink GPU or 8PCIe GPU which is specially designed for AI/HPC
• Highest Density: 8 FHFL GPU get emerged in the limited space of 2U chassis
• Highest Performance: support the latest optimized NVLink2.0 with fully connection topology, up to 960 Tensor FLOPs and 376 TOPS on INT8.
• Highest Flexibility: abundant topology for GPU to meet different work load, free to choose different GPUs.
Application scenarios:
• AI field----Intelligent Security, Intelligent Traffic, Intelligent Finance, Intelligent Medical and Intelligent Manufacture that need data analysis and training
• HPC------High performance clusters pursuing ultra-high performances, such as rendering operation, CAD and CAE.
• Video acceleration------Complex and diverse video processing that need paralleled operation for government, education.
IEI is een van de grootste leveranciers van producten voor industriële computersystemen. IEI levert honderden verschillende boards, systemen en onderdelen voor uiteenlopende applicaties in de industriële automatisering, defensie, medisch, infotainment en mobiel gebruik. Vooruitstrevende oplossingen bezorgen u als klant een kortere ontwerpcyclus zodat u de voorsprong op de concurrent kunt behouden en zelfs vergroten.
In this deck from Switzerland HPC Conference, Gunter Roeth from NVIDIA presents: Deep Learning on the SaturnV Cluster.
"Machine Learning is among the most important developments in the history of computing. Deep learning is one of the fastest growing areas of machine learning and a hot topic in both academia and industry. It has dramatically improved the state-of-the-art in areas such as speech recognition, computer vision, predicting the activity of drug molecules, and many other machine learning tasks. The basic idea of deep learning is to automatically learn to represent data in multiple layers of increasing abstraction, thus helping to discover intricate structure in large datasets. NVIDIA has invested in SaturnV, a large GPU-accelerated cluster, (#28 on the November 2016 Top500 list) to support internal machine learning projects. After an introduction to deep learning on GPUs, we will address a selection of open questions programmers and users may face when using deep learning for their work on these clusters."
Watch the video: http://wp.me/p3RLHQ-gDv
Learn more: http://www.nvidia.com/object/dgx-saturnv.html
and
http://hpcadvisorycouncil.com/events/2017/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
6. 6
AMPERE GPU ARCHITECTURE
Streaming Multiprocessor (SM)
GA100 (A100, A30) GA102 (A40, A10)
32 FP64 CUDA Cores
64 FP32 CUDA Cores
4 Tensor Cores
Up to 128 FP32
CUDA Cores
1 RT Core
4 Tensor Cores
7. 7
DATA CENTER PRODUCT COMPARISON (SEPT 2021)
* Performance with structured sparse matrix
A100* A30* A40 A10 T4
Performance
FP64 (no Tensor Core) 9.7 TFlops 5.2 TFlops - - -
FP64 Tensor Core 19.5 TFlops 10.3 TFlops N/A N/A N/A
FP32 (no Tensor Core) 19.5 TFlops 10.3 TFlops 37.4 Tflops 31.2 TFlops 8.1 TFlops
TF32 Tensor Core 156 | 312 TFlops* 82 | 165 Tflops* 74.8 | 149.6 TFlops* 62.5 | 125 TFlops* N/A
FP16 Tensor Core 312 | 624 Tflops* 165 | 330 TFlops* 149.7 | 299.4 TFlops* 125 | 250 TFlops* 65 TFlops
BFloat16 Tensor Core 312 | 624 Tflops* 165 | 330 TFlops* 149.7 | 299.4 TFlops* 125 | 250 TFlops* N/A
Int8 Tensor Core 624 | 1248 TOPS* 330 | 661 TOPS* 299.3 | 598.6 TOPS* 250 | 500 TOPS* 130 TOPS
Int4 Tensor Core 1248 | 2496 TOPS* 661 | 1321 TOPS* 598.7 | 1197.4 TOPS* 500 | 1000 TOPS* 260 TOPS
Form Factor
SXM4 module
on baseboard
x16 PCIe Gen4
2 Slot FHFL
3 NVLINK bridges
x16 PCIe Gen 4
2 Slot FHFL
1 NVLINK bridge
x16 PCIe Gen4
2 Slot FHFL
1 NVLINK bridge
x16 PCIe Gen 4
1 Slot FHFL
x16 PCIe Gen 3
1 Slot LP
GPU Memory 40 | 80 GB HBM2e 40 | 80 GB HBM2e 24 GB HBM2 48 GB GDDR6 24 GB GDDR6 16 GB GDDR6
GPU Memory Bandwidth 1555 | 1935 GB/s 1555 | 2039 GB/s 933 GB/s 696 GB/s 600 GB/s 300 GB/s
Multi-Instance GPU Up to 7 Up to 7 Up to 4 N/A N/A N/A
Media Acceleration
1 JPEG Decoder,
5 Video Decoder
1 JPEG Decoder
4 Video Decoder
1 Video Encoder,
2 Video Decoder
(+AV1 decode)
1 Video Encoder,
2 Video Decoder
Ray Tracing No No No Yes Yes Yes
Graphics
For in-situ visualization
(no vPC/vQuadro)
Best Better Good
Max Power 400 W 250 | 300 W 165 W 300 W 150 W 70 W
8. 8
TF32 TENSOR CORE TO SPEEDUP FP32
Range of FP32 and Precision of FP16
Input in FP32 and Accumulation in FP32
FP32
Matrix
FP32
Matrix
FP32
Matrix
Format to TF32
and Multiply
FP32 Accumulate
23 bits
8 bits
10 bits
10 bits
7 bits
8 bits
5 bits
8 bits
FP32
TF32
FP16
BFloat16
Sign Range Precision
TF32 Range
TF32 Precision
15. 15
ALTAIR CFD (SPH SOLVER)
Ampere PCIe scaling performance
Aerospace Gearbox
Size: ~21M Fluid particles (~26.7M total)
1000 timesteps
Higher is Better
1.0 1.0
0.5
1.0
1.8 1.8
1.0
1.7
3.5 3.5
1.9
3.4
6.2 6.3
3.6
6.1
0X
1X
2X
3X
4X
5X
6X
7X
A100 80GB PCIe A100 40GB PCIe A30 PCIe A40 PCIe
Relative
Performance
Aerospace Gearbox 26M
Altair CFD SPH Solver (Altair® nanoFluidX®) on NVIDIA EGX Server
1 GPU 2 GPUs 4 GPUs 8 GPUs
Tests run on a server with 2x AMD EPYC 7742@2.25GHz 3.4GHz Turbo (Rome), 64-core CPU, Driver 465.19.01, 512GB RAM, 8x NVIDIA GPUs,
Ubuntu 20.04, ECC off, HT Off
Relative performance calculated based on the average model performance (nanoseconds/particles/timesteps) on Altair nanoFluidX 2021.0
16. 16
ROCKY DEM 4.4
ROTATING DRUM Benchmark with polyhedron and spherical shaped particles
0
10
20
30
40
50
60
70
80
90
100
Polyhedron Cells Performance on V100
1 x V100
2 x V100
4 x V100
Speed-up
(relative
to
8xCPU
cores
Intel
Xeon
Gold
6230
CPU
at
2.10
GHz
31.25 62.5 125 250 500 1000 2000
Number of particles per GPU (x1000)
0
10
20
30
40
50
60
70
80
90
100
Polyhedron Cells Performance on A100
1 x A100
2 x A100
4 x A100
Speed-up
(relative
to
8xCPU
cores
Intel
Xeon
Gold
6230
CPU
at
2.10
GHz
31.25 62.5 125 250 500 1000 2000
Number of particles per GPU (x1000)
Higher is Better Higher is Better
38x speedup for 1xV100 when compared with an 8-core CPU Intel Xeon Gold 6230 @ 2.10GHz
47x speedup for 1xA100 when compared with an 8-core CPU Intel Xeon Gold 6230 @ 2.10GHz
Case Description: Particles in a drum rotating at 1 rev/sec, simulated for 20,000 solver iterations
Hardware on Oracle cloud: CPU: Intel Xeon Gold 6230 @2.1 GHz (8 cores)GPU: NVIDIA A100 and V100
21. 21
GIANT MODELS PUSHING LIMITS OF EXISTING ARCHITECTURE
Requires a New Architecture
GPU 8,000 GB/sec
CPU 200 GB/sec
PCIE Gen4 (Effective Per GPU) 16 GB/sec
Mem-to-GPU 64 GB/sec
System Bandwidth Bottleneck
DDR4 HBM2e
GPU
GPU
GPU
GPU
x86
ELMo (94M)
BERT-Large (340M)
GPT-2
(1.5B)
Megatron-LM
(8.3B)
T5 (11B)
Turing-NLG
(17.2B)
GPT-3 (175B)
0.00001
0.0001
0.001
0.01
0.1
1
10
100
1000
2018 2019 2020 2021 2022 2023
Model
Size
(Trillions
of
Parameters)
100 TRILLION PARAMETER MODELS BY 2023
22. 22
NVIDIA GRACE
Breakthrough CPU Designed for Giant-Scale AI and HPC Applications
FASTEST INTERCONNECTS
>900 GB/s Cache Coherent NVLink CPU To GPU (14x)
>600GB/s CPU To CPU (2x)
NEXT GENERATION ARM NEOVERSE CORES
>300 SPECrate2017_int_base est.
Availability 2023
HIGHEST MEMORY BANDWIDTH
>500GB/s LPDDR5x w/ ECC
>2x Higher B/W
10x Higher Energy Efficiency
23. 23
TURBOCHARGED TERABYTE SCALE ACCELERATED COMPUTING
Evolving Architecture For New Workloads
CURRENT x86 ARCHITECTURE
DDR4 HBM2e
INTEGRATED CPU-GPU ARCHITECTURE
LPDDR5x HBM2e
3 DAYS FROM 1 MONTH
Fine-Tune Training of 1T Model
GPU
GPU
GPU
GPU
GRACE
GRACE
GRACE
GRACE
GPU
GPU
GPU
GPU
x86
Transfer 2TB in 30 secs Transfer 2TB in 1 secs
GPU 8,000 GB/sec
CPU 200 GB/sec
PCIE Gen4
(Effective Per GPU)
16 GB/sec
Mem-to-GPU 64 GB/sec
GPU 8,000 GB/sec
CPU 500 GB/sec
NVLink 500 GB/sec
Mem-to-GPU 2,000 GB/sec
REAL-TIME INFERENCE
ON 0.5T MODEL
Interactive Single Node NLP Inference
Bandwidth claims rounded to nearest hundred for illustration.
Performance results based on projections on these configurations Grace : 8xGrace and 8xA100 with 4th Gen NVIDIA NVLink Connection between CPU and GPU and x86: DGX A100.
Training: 1 Month of training is Fine-Tuning a 1T parameter model on a large custom data set on 64xGrace+64xA100 compared to 8xDGXA100 (16xX86+64xA100)
Inference: 530B Parameter model on 8xGrace+8xA100 compared to DGXA100.
24. 24
NVIDIA 秋の HPC Weeks
Week 1 : 2021 年 10 ⽉ 11 ⽇ (⽉) GPU Computing & Network Deep Dive
Week 2 : 2021 年 10 ⽉ 18 ⽇ (⽉) HPC + Machine Learning
Week 3 : 2021 年 10 ⽉ 25 ⽇ (⽉) GPU Applications
https://events.nvidia.com/hpcweek/
Stephen W. Keckler
NVIDIA
Torsten Hoefler
ETH Zürich
⻘⽊ 尊之
東京⼯業⼤学
Tobias Weinzierl
Durham University
James Legg
University College London
Mark Turner
Durham University
岡野原 ⼤輔
Preferred Networks
横⽥ 理央
東京⼯業⼤学
美添 ⼀樹
九州⼤学
秋⼭ 泰
東京⼯業⼤学
市村 強
東京⼤学
⾼⽊ 知弘
京都⼯芸繊維⼤学
25. 25
SUMMARY
Current NVIDIA data center GPU
A100 & A30 for FP64, A40 & A10 for FP32
GPU accelerated application performance
Simulia CST Studio, Altair CFD and Rocky DEM got excellent performance on GPU
Paraview with GPU acceleration
Ray tracing accelerated with RT core
In future
Grace CPU improve memory bandwidth between CPU and GPU
