Demystifying AI and ML Infrastructure for Network Engineers
Discover how to build and optimize GPU clusters for AI workloads, focusing on inter-GPU networking, efficient communication protocols, and practical implementation using Cisco's infrastructure solutions.
Kickstart AI in Your Data Center with Cisco Validated Designs
Discover how to efficiently deploy AI infrastructure using Cisco's validated designs, optimized hardware stacks, and partner integrations for seamless enterprise-scale AI implementation and workload management.
VMware Private AI Foundation with NVIDIA - Technical Overview and Demo
Discover how VMware's Private AI Foundation with NVIDIA streamlines AI development through automated infrastructure management, RAG capabilities, and simplified GPU resource allocation for enterprise applications.
High Performance Scalable Support for Big Data Stacks with MPI - Leveraging MPI4Spark and MPI4Dask
Discover how MPI4Spark and MPI4Dask enhance big data frameworks by leveraging high-speed networks and HPC systems for improved performance and scalability in distributed computing environments.
iCheck - Leveraging RDMA and Malleability for Application-Level Checkpointing in HPC Systems
Discover how RDMA and malleable multilevel checkpointing can enhance HPC system reliability through iCheck framework, offering improved performance and efficient data transfer mechanisms for supercomputing applications.
Infrastructure Software for Hyperscale AI and GPU Clusters
Explore cutting-edge infrastructure software solutions powering large-scale AI operations and GPU cluster management for hyperscale computing environments.
Memory Expansion Requirements for AI Systems in Hyperscale Data Centers
Explore evolving memory architecture requirements for AI systems in hyperscale data centers, focusing on GPU acceleration and infrastructure optimization for next-generation applications.
Accelerator Clusters: The New Supercomputer
Discover how accelerator clusters are revolutionizing supercomputing through NVIDIA's innovative approach to high-performance computing architecture and interconnect technologies.
Unified Collective Communication - A Library for CPU, GPU, and DPU Integration
Explore the innovative Unified Collective Communication library that seamlessly integrates CPU, GPU, and DPU communication, enhancing performance across diverse computing architectures.
Demystifying the Communication Characteristics for Distributed Transformer Models
Explore communication patterns and optimization strategies for distributed transformer models, focusing on collective operations and performance characteristics in large-scale deployments.
Rail-only: A Low-Cost High-Performance Network for Training LLMs with Trillion Parameters
Discover a groundbreaking network architecture designed for efficient training of large language models, optimizing performance while reducing infrastructure costs.
Sharing is Caring - Fractional GPU Allocations with MetaGPU Device Plugin
Discover how to optimize GPU resource allocation in Kubernetes using MetaGPU Device Plugin, enabling fractional GPU sharing and efficient scheduling for enhanced cluster performance.
PyTorch Tutorial: Deep Learning Framework Fundamentals - Fall 2022
Master PyTorch fundamentals and deep learning concepts, from tensor operations to GPU acceleration, autograd, and data loading - all through hands-on coding examples and practical implementations.
Что я хотел бы знать об MLOps год назад - Практический опыт внедрения Machine Learning
Discover essential MLOps insights through real-world experiences at Kolesa Group, covering ML model deployment, Docker, Kubernetes integration, and practical monitoring strategies for machine learning systems.
Memory Semantics and Data Movement with CXL and SDXI - Standards and Applications
Explore memory semantics and data movement standards through CXL and SDXI technologies, focusing on accelerator-assisted operations and heterogeneous computing architectures.
Fast Deep Learning with Categories
Explore GPU-accelerated deep learning using Haskell, ConCat, and Accelerate for high-performance industrial applications.
The Best Multicore-Parallelization Refactoring You've Never Heard Of
Discover a powerful, lesser-known refactoring technique for optimizing multicore parallelization in software development.
Immutable Arrays Help Mutating Arrays More Efficiently - ML'23 Lecture
Explore how immutable arrays enable compiler optimizations for efficient implementations, challenging intuitions about low-level control in array computations.
A Tale of Shared Memory Concurrency in Programming Languages
Explore shared memory concurrency in programming languages, examining challenges, recent advancements, and future directions for designing effective concurrency models for multicore systems.
Comparing Elixir and Python for Simple Neural Networks
Compare Elixir's Nx library with Python's machine learning ecosystem for training neural networks. Explore development experiences and performance differences using MNIST and CIFAR-10 datasets.
From FP to FBP: Synthesizing Processing Elements for Stream Computing
Explore Flow-Based Programming in Erlang, learning to design dataflow graphs and generate code for stream computing. Synthesize processing elements from functional blocks for efficient parallel data processing.