
Explore MFEM, a parallel C++ library for finite element methods. Learn to develop scalable algorithms for various platforms, from laptops to supercomputers.

Explore Netgen/NGSolve finite element software: overview, Python scripting, teaching applications, and recent developments in matrix-valued elements, GPU utilization, and time-dependent problems.

Explore MFEM's high-order mathematical calculations for scientific simulations, including key capabilities, examples, and future plans. Learn about the project's growth and recent software release.

Explore Palace, a parallel finite element code for full-wave electromagnetics simulations, and its capabilities for large-scale 3D modeling in quantum computing hardware design.

Explore MFEM's key capabilities, recent developments, and future directions in high-order finite element methods for scientific simulations.

Explore recent developments in MFEM, including sub-mesh extraction, GPU-based calculations, and integrations with scientific software libraries for high-order finite element simulations.

Discover how Prestissimo revolutionizes Presto SQL engine with C++ runtime, delivering enhanced performance, efficient memory management, and improved CPU utilization through vectorization and runtime optimizations.

Explore the CXL 3.0 specification's advanced features for heterogeneous computing, including enhanced memory sharing, coherency improvements, and expanded fabric capabilities for high-performance workloads.

Explore the evolution of CXL technology and server fabrics, focusing on memory management, resource pooling, and system architecture improvements for enhanced computing performance.

Explore high-performance computational storage solutions for data analytics, focusing on UCIe standards, chiplet interoperability, and power-efficient performance across cloud, edge, and client computing infrastructures.

Explore real-time data assimilation for digital twins using particle filtering techniques, with interactive demonstrations and discussions on parallelization, future applications, and implementation challenges.

Learn to accelerate GPU analytics using RAPIDS for Apache Spark and Alluxio, enabling faster data access and processing for AI and analytics workloads across any cloud environment.

Explore a collaborative data science workflow using Kedro pipelines, MLflow tracking, and cloud-agnostic GPU containers for efficient model development, training, and production deployment.

Explore the integration of Presto and Spark, combining low-latency evaluation with robust execution for enhanced SQL experience in both interactive and batch use cases at Facebook scale.

Explore GPU acceleration for data science with RAPIDS, offering significant performance gains over CPU-only solutions. Learn its functionality, architecture, and integration with Apache Spark for large-scale computation.

Discover how to implement a scalable demand forecasting solution using Databricks and MLflow, improving retail efficiency and reducing food waste through accurate production and delivery planning.

Explore real-time video analytics optimization with Reducto, a filtering technique for efficient on-camera processing. Learn about system design, performance results, and practical applications.

Explore large-scale simulation framework for Hyperloop design, utilizing cloud computing, data pipelines, and machine learning to address key technical and business questions for safe and efficient mass transportation.

Explore high-performance middleware design for exascale systems, focusing on MVAPICH2 project's features and performance in HPC, AI, and data science applications across various platforms.

Explore Docker's role in Data Science, Machine Learning, and AI, including GPU processing techniques and Industrial AI demos, presented by experts from Baker Hughes.

Learn to deploy and scale machine learning models efficiently using Apache Beam for distributed inference on CPUs and GPUs, with practical insights on parallelizing workloads.