SUPERCOMPUTER GPU ARTIFICIAL INTELLIGENCE CONSULTING

• Top Investment Banking / data center Competitive Advantages Using Machine Learning (ML)  

• Machine Learning (ML) provides a technological breakthrough for the Financial / data center Industry that provides millisecond decision improvements that translate into more profitable decisions

• Developing ML solutions for the Financial  / data center Industry is one of the riskiest, most costly and time-sensitive tasks facing this industry. 

• Developing new ML applications for the Financial / data center  Industry & running these applications with high performance architectures requires experienced practitioners with stellar track records.

• Rapid entry into the Graphics Processing Unit (GPU) Artificial Intelligence/Machine Learning market arena - Stock Trading & data centers. Better GPU selection (Nividia, AMD, Intel)

• Faster decisions, faster stock trades and better stock trade & decision accuracy

• Reduced risks in R/D
• Reduced costs in R/D
• Reduced schedules in R/D

• Situation: CEO/President, large investment / data center firm. 

• Needs: Projected yearly growth revenue & profits of 30%.  Competitive advantage that improves trade / data center decisions & accuracy. Decreases in R&D costs, risks and scheduling 

• Reason:  Electronic trading & data center staff is not keeping up with new advances in supercomputer GPU architecture software/hardware & artificial intelligence mathematics. Poor R/D planning & tracking & costs are high. Integration schedules are lengthy. Less funding available for innovation projects

• FALCON ML Consulting Solution:

• High Performance Computing (HPC) Graphics Processing Unit (GPU) & Artificial Intelligence (AI) consulting company descendant of the Oryx Corporation

• Oryx Corp. Founder: John Hiller

• Dr. Narendra Ahuja – Artificial Intelligence & Computer Science (Azriel Rosenfeld PhD Advisor) – Student Advisees 60 PhD, 20 MS, 100 Undergrad Research, 14 postdocs, University of Illinois – Labs developed Computer Vision & Robotics Lab Beckman Institute (Research)

• Oryx Corp. developed both commercial and militarized Supercomputer (High Performance Computer)

• Used by DARPA for the first successful Autonomous Land Vehicle (AI)

• $19M Funded by Eastman Kodak Ventures, Grumman Corp., Allied Signal and top Venture Capital firms.

• One of the earliest developers/users of Graphical User Interface programing software for math

• Oryx Corp. Accomplishments:  

• Designed/Completed/Tested a Crossbar-based Supercomputer within 18 months

• Experience in Development/Testing rich library of Parallel math algorithms used in sonar/image/radar processing applications requiring high computational and memory bandwidth applicable to Electronic Trading & data center AI and Machine Learning (ML)

• Linear Algebra

• Neural Net

• Vision Processing, Optimization

• Results:

• Rapid entry into the Graphics Processing (GPU) Artificial Intelligence/Machine Learning market arena - Stock Trading & data centers. Better GPU selection (Nividia, AMD or Intel)
• Faster decisions, faster stock trades and better stock trade & decision accuracy
• Reduced risks in R/D
• Reduced costs in R/D
• Reduced schedules in R/D
    

• Sponsors: Eastman Kodak, Grumman, General Electric, Martin Marietta, Unisys, Lockheed, Raytheon, etc.
• Applications: Radar, sonar, image processing, electronic warfare, autonomous land vehicle, communications intercept, etc.
• Founder: John Hiller (Founder, CEO, CTO)
• Consultants: Dr. Ariel Rosenfeld (Father of Image Processing), U Maryland. Dr. Rosenfeld - lead architect of the DARPA benchmark suite – Hough Transform (finding straight lines in an image), Traveling Salesman, etc.
• Products: Oryx SSP R&D development computer architecture hardware & software broke world records for DARPA & Linpack Algorithms---directly applicable to efficiently provide the advantages necessary for millisecond quickness in making better data center & trading decisions.

• Oryx initial development funding ($3.6M) – all technical milestones (hardware, Math algorithms and Flowgraph editor) demonstrated on a parallel processor in an 18-month work schedule
• Oryx 2nd round funding of $15M from top venture firms in the USA
• New Enterprise Associates, Oxford Partners, Polyventures, Grumman Ventures, East Kodak Ventures, Spectra Ventures, Investech, & Allied Signal Corporate. 
• Delivered on-board units to Grumman, Lockheed, etc

•  Highly parallel computer architecture employing crossbar switch with selectable pipeline delay
• United States Patent 5081575

• A crossbar switch which connects N (N=2k ; k=0, 1, 2, 3) coarse grain processing elements (rated at 20 million floating point operations per second) to a plurality of memories provides for a parallel processing system free of memory conflicts over a wide range of arithmetic computations (i.e. scalar, vector and matrix). The configuration of the crossbar switch, i.e., the connection between each processing element unit and each parallel memory module, may be changed dynamically on a cycle-by-cycle basis in accordance with the requirements of the algorithm under execution. Although there are certain crossbar usage rules which must be obeyed, the data is mapped over parallel memory such that the processing element units can access and operate on input streams of data in a highly parallel fashion with an effective memory transfer rate and computational throughput power comparable in performance to present-day supercomputers. The crossbar switch is comprised of two basic sections; a multiplexer and a control section. The multiplexer provides the actual switching of signal paths, i.e. connects each processing element unit to a particular parallel memory on each clock cycle. The control section determines which connections are made on each clock cycle in accordance with the algorithm under execution. Selectable pipelined delay in the control section provides for optimal data transfer efficiency between the processors and memory modules over a wide range of array processing algorithms. The crossbar switch also provides for graceful system degradation in computational throughput power without the need to download a new program.

• Method for Automated Deployment Of Software Program onto a Multi-Processor Architecture
• United States Patent 5418953

• The method is employed for pre-assignment & scheduling of tasks that enables allocation across multiple physical processors arranged in a variety of architectures.  The assigning action attempts to arrive at minimal cost value for all tasks comprising the problem

• Multiple processing elements containing floating point, fixed point & logical on computational nodes (2,4,8,etc) connected to multiple memories(2,4,8,etc) via Crossbar Switch
   
• Scalar, vector, & matrix computation capabilities

• Fully connected Parallel Crossbar Switch free of memory access conflicts over a wide range of applications
   
• High & effective memory transfer rates
   
• Data mapped to parallel memories that are readily accessible for input/output & matrix processing

• This Supercomputer architecture  and the math running on it provide a method for comparing (selecting Nividia, AMD or Intel) 2025 Graphics Processing Unit (GPU) systems to gain the latest Artificial Intelligence application high performance.

Alma mater

University   of Maryland, College Park

Known for

Face detection, Video understanding, Motion planning, 3D Vision, Computational cameras, Pattern Recognition

Awards

IEEE Emanuel R. Piore Award (1999)
IEEE Fellow (1992)
ACM   Fellow (1996)
Presidential Young Investigator Award (1984)
AAAI Fellow (1992)
SPIE Technology   Achievement Award (1998)

Scientific career

Fields

Computer science, Computer vision, Artificial Intelligence, Machine   Learning, Robotics

Institutions

Professor,  University of Illinois, Urbana-Champaign
   Founding Director, International   Institute of Information Technology, Hyderabad

Founding Director, Information Technology Research Academy, Delhi

Thesis

Mosaic Models for Image Analysis and Synthesis (1979)

Doctoral advisor

Azriel Rosenfeld

• Computer vision, advanced artificial intelligence, machine learning/pattern recognition, computer architecture, probability theory, robotics, virtual environments, digital signal processing and knowledge networks
• Computational approach to automatically extract syntax of images & use it for automated image understanding
• Special purpose cameras
• Understanding 3D scenes from Stereo, Motion and Texture
• Machine learning: Efficient, Explainable, Physics inspired, Multimodal
• Algorithms and computational complexity
• PhD in computer science from University of Maryland, College Park MD advised by AzrielRosenfeld (Father of Image Processing) – Thesis Mosaic Models for Image Analysis and Synthesis – Azriel Rosenfeld Advisor
• Publications: 3 Books Co-Authored, 20 Book Chapters, 115 Journal Articles, 350 Conference Papers, 4 Patents
• Student Advisees: 60 PhDs, 30 MS
• •New Labs Developed: Computer Vision and Robotics Lab (Research), Robotics (Teaching)
• New Courses Started: Computer Vision, Pattern Recognition, Robotics
• Consulting: SAIC, Battelle Corp., AT&T, Lockheed, Eastman Kodak, Honeywell, Westinghouse, HRL Labs
• Development: Automated rail inspection; Active and Passive Cameras - omnifocus, hemispherical, high-dynamic-range, stereo; Fingerprint recognition

Patents

·N. Ahuja and M. Tabb, Multiscale Image Edge and Region Detection Method and Apparatus, U. S. Patent, Issued September 1998. ·

R. Dugad and N. Ahuja, Transform Domain Significant Coefficient Digital Image Watermarking Method, U.S. patent Application filed, June 2000. ·

H. Hua and N. Ahuja, Method and Apparatus for a High-Resolution and Real-Time Panoramic Camera, Patent Application Filed, November 2001.

 ·

A. Krishnan and N. Ahuja, Imaging Apparatus and Method for Determining Range from Focus and Focus Information, U. S., Patent, Issued September 1995. European Patent issued, October 2001.

·

H. Hua and N. Ahuja, Method and Apparatus for a High-Resolution and Real-Time Panoramic Camera, Patent Application Filed, November 2001. ·

R. Dugad and N. Ahuja, Transformation of Image Parts in Different domains to Obtain Resultant Image Size Different From Initial Image Size, U.S. Patent, Issued October 2004. 

Books Authored

·N. Ahuja and B. Schachter, Pattern Models, Wiley, l983. ·

J. Weng, T. S. Huang and N. Ahuja, Motion and Structure from Image Sequences, Springer-Verlag, 1992. 

·

Ming-Hsuan Yang and N. Ahuja, Face Detection and Hand Gesture Recognition for Vision-Based Human Computer Interaction, Kluwer Academic Publishers, 2001.  

• 128 (27)SM/GPU 
• 32(26) CudaCores/SM
• Total # Cores 4096 (213)/GPU
• Two FP32 Mult or Add/CudaCore
• 64 FP32 Per SM
• One FP64 Multor Add /CudaCore
• 32 FP64 Per SM
• 40 GB HBM2 Total – 6 per GPU
• 40 MB L2 cache Total – 6 per GPU
• L1 data cache = 128 per GPU, ----192 Kbytes per L1
• Each Cuda Core has 8 Kbytes– 4 Register Files
• 1.2 Ghz. clock

• Dense Memories to L1 transfer: 27,306/41,130 = 66.4%
• L1 to CUDA core (RFs) transfer: 8,448/ 41,130 = 20.5%
• CUDA core FP320s: 4,096/41,130 = 10.0%
• CUDA core FP321s: 4,096 +256 = 5,120/41,130 = 12.4%

•

• Host Gen 4 PCI to 6 Device Memories 32 Gbytes/sec transfer of 786,432 bytes = 25 microseconds
• 6 Device Memories to 6 L2s 177Gbytes/sec transfer of 786,432 bytes = 4.5 microseconds
• 6 L2s to 128 L1s 280Gbytes/sec transfer of 786,432 bytes = 2.8 microseconds
• L1 to 32 CUDA Cores 10.6 Gbytes/sec transfer of of 137,216 bytes = 13.0 microseconds
• CUDA COREs Computation using 1.2 Ghz.clock (.833 nanoseconds per clock) of 13,824 clocks = 10.7 microseconds
• Total runtime = 56 microseconds plus software overhead

• 27,306 clocks 5,461 clocks for movement of A Matrix evenly distributed across 6 L2s---each A row is sent to a pair of L1s---64 pairs of L1s hold the A Matrix. 16,384clocks for movement of one half of B matrix into 2 L2s---one L2 broadcasts to 64 of 128 L1s---the other L2 broadcasts to the other 64 L1s. Data in & out of Dense Memories, L2s, L1s. 5,461clocks to move C matrix back to dense memories via L2s.
• 13,824 clocks 4 passes (1024 C matrices per pass)
• 41,130 clocks total 
• 256 X 256 C matrix in 6 dense memories tied to host bus

• Dense Memories to L1 transfer: 174,762/188,458 = 92.7%
• L1 to CUDA core transfer: 2,528/ 188,458 = 1.3%
• ALUs:   11,168/188,458 = 5.9%

• Host Gen 4 PCI to 6 Device Memories 32 Gbytes/sec transfer of 8,388,608 bytes = 262 microseconds
• 6 Device Memories to 6 L2s 177Gbytes/sec transfer of 786,432 bytes = 47 microseconds
• 6 L2s to 128 L1s 280Gbytes/sec transfer of 786,432 bytes = 30 microseconds
• L1 to 32 CUDA Cores 10.6 Gbytes/sec transfer of of 65,536 bytes = 6.2 microseconds
• CUDA COREs Computation using 1.2 Ghz.clock (.833 nanoseconds per clock) of 11,168 clocks = 9.3 microseconds
• Total runtime = 354 microseconds plus software overhead

For the third quarter of fiscal year 2025 (ending October 26, 2025), NVIDIA's gaming GPU sales revenue was approximately $4.2 billion. This was part of the company's record $57 billion total revenue for the quarter, with the Data Center business generating the majority at $51.2 billion. The remaining $5.8 billion was from the Gaming, Professional Visualization, and Automotive segments combined. 

• Gaming      Revenue: $4.2 billion for the third quarter of fiscal      year 2025.
• Data      Center Revenue: $51.2 billion for the third quarter      of fiscal year 2025.
• Total      Revenue: $57 billion for the third quarter of fiscal      year 2025. 

Nvidia's revenue from GPU sales in the third quarter of fiscal year 2025 was approximately $4.3 billion from the Gaming segment, plus an unspecified portion of its larger Data Center segment, which is driven by AI GPUs. For this specific quarter (Q3 FY2026, ended October 26, 2025), the Data Center segment generated $51.2 billion, representing 89.8% of the total $57 billion revenue. 

• Gaming      GPUs: Revenue was $4.265 billion, up 30% year-over-year.
• Data      Center: This segment, driven by AI and data center GPUs,      generated $51.215 billion. This is the vast majority of Nvidia's total      revenue, as the company announced its fiscal year 2025 has ended and      fiscal year 2026 has begun.
• Total      Revenue: Nvidia reported record total revenue of $57 billion for      the third quarter of fiscal year 2026. 

Nvidia Projects Quarterly Sales of $65 Billion, AI Demand Strong

What is Nvidia's biggest source of income?

NVIDIA's AI and data center business is responsible for the majority of its revenue today. Companies like OpenAI, Google, Meta, Microsoft, Amazon, Tesla, and nearly every AI startup rely on NVIDIA GPUs. The main driver behind NVIDIA's multi-trillion valuation. Before AI exploded, NVIDIA was known for its gaming GPUs.

Artificial intelligence math, which primarily consists of massive amounts of linear algebra equations (matrix multiplications and summations), runs exceptionally well on NVIDIA data center GPUs. The architecture of GPUs, designed for parallel processing, makes them the de facto standard for accelerating AI workloads over traditional CPUs. 

• Parallel      Processing: The core function of AI models involves countless      independent calculations that can be performed simultaneously. GPUs      contain thousands of simple processing cores that can handle these      operations in parallel, unlike CPUs which have fewer, faster cores optimized      for sequential tasks.
• Tensor      Cores: NVIDIA developed specialized processing units within its GPUs      called Tensor Cores. These cores are engineered to perform      matrix multiply-accumulate (MMA) operations in a single instruction, which      is the most common and computationally intensive math in neural networks.      Each successive generation (Volta, Ampere, Hopper, Blackwell) has improved      their power and efficiency, introducing support for various precision      formats like FP16, BF16, and FP8.
• Software      Ecosystem (CUDA): NVIDIA provides a comprehensive software platform      called CUDA, which allows developers to harness the parallel processing      power of their GPUs efficiently. The NVIDIA Math Libraries, such as cuBLAS and cuSPARSE,      are highly optimized for AI and scientific computing tasks, enabling      significant speed-ups with minimal code changes.
• High      Memory Bandwidth: AI models require processing large datasets quickly.      NVIDIA's data center GPUs, like the H100 and H200, feature high memory      bandwidth (e.g., the H200 has 4.8 TB/s of bandwidth) and large memory      capacities to move data rapidly between the processor and memory, which is      crucial for performance. 

NVIDIA offers specialized hardware and software for the entire AI lifecycle, from training massive models to inference and deployment in data centers. 

• DGX      Systems: These are integrated, "one-stop-shop" AI      supercomputing platforms designed specifically for large-scale AI      training.
• A100 (see above)      and H100/H200 GPUs: These are the flagship data center GPUs, widely      used by major cloud providers (AWS, Azure, Google Cloud) and AI labs      (OpenAI, Meta) to train and run large language models (LLMs) like ChatGPT.
• AI      Enterprise Software: Tools like the NVIDIA NeMo      framework and NIM (NVIDIA Inference Microservices) provide frameworks      for building, customizing, and deploying AI models efficiently. 

Latest GPU market analysis shows Nvidia losing ground to AMD

The latest quarterly GPU market share figures have been released by specialist analyst outfit Jon Peddie Research (JPR). It observes that, during Q3 2025, the PC add-in-board (graphics card) market grew to 12.0 million units, which is 2.8% up vs the previous quarter. However, probably more interesting are the figures showing that AMD and Intel are gaining market share – at the expense of Nvidia, of course (as it is a three-horse race).