From the course: NVIDIA Certified Associate AI Infrastructure and Operations (NCA-AIIO) Cert Prep
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NVIDIA: Powering AI GPU innovation - NVIDIA Tutorial
From the course: NVIDIA Certified Associate AI Infrastructure and Operations (NCA-AIIO) Cert Prep
NVIDIA: Powering AI GPU innovation
I hope by this time you have got a basic understanding of all the fundamental required for learning NVIDIA technology and passing the certification. Let's now go to the core concept of NVIDIA technology stack. I want to talk a little bit on the history of NVIDIA, how this innovation actually started. So the starting point for NVIDIA came in 1993 when it was founded. And in 1995, they came up with a graphic card called NV1. They started looking into enhancing this graphic capability. And that's where they came up with something called GeForce 256. That was first product NVIDIA called a GPU. So the term or the name is coined by NVIDIA called GPU, Graphics Processing Unit. So it started with gaming routes. Then they realized there's a potential of using GPUs for programmability. So they expanded this concept to other methods through GPU core programmability or CPU core programmability. And then they have came up with parallel compute architecture. So in 2012-17 time frame, they came up…
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NVIDIA: Powering AI GPU innovation2m 37s
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NVIDIA technology stack3m 12s
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Layer 1: Physical layer3m 53s
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GPU on a graphics card1m 57s
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DGX platform2m 56s
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DGX SuperPOD1m 57s
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ConnectX1m 49s
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BlueField DPUs2m 32s
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NVIDIA reference architectures1m 38s
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Understanding GPU cores5m
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Comparing GPU cores4m 18s
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NVIDIA DGX platform: Timeline4m 47s
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DGX platform: Deployment options3m 38s
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DGX A100 vs. H1004m 6s
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Layer 2: Data movement and I/O acceleration59s
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NVLink8m 5s
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InfiniBand2m 5s
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InfiniBand vs. Ethernet1m 43s
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DMA and RDMA6m 30s
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GPUDirect RDMA2m 44s
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GPUDirect storage1m 45s
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Quick comparison1m 56s
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Layer 3: OS, driver, and virtualization2m 17s
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GPU drivers4m 38s
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GPU virtualization5m 8s
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vGPU vs. MIG, part 17m 48s
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vGPU vs. MIG, part 210m 59s
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Layer 4: Core libraries6m 44s
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Compute unified device architecture (CUDA)3m 12s
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Installing CUDA2m 11s
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NVIDIA collective communications library (NCCL)3m 41s
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NVLink, NVSwitch, PCIe, RDMA vs. NCCL3m 44s
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Layer 5: Monitoring and management2m 23s
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NVIDIA-SMI4m 24s
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Data Center GPU Manager (DCGM)7m 27s
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Base Command Manager5m 33s
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Which one to use?2m 3s
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Layer 6: Applications and vertical solutions3m 48s
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Summary2m 26s
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NVIDIA AI Enterprise3m 2s
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NVIDIA AI Factory2m 24s
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