Big Data Computing
Comprehensive exploration of big data technologies, covering Hadoop, Spark, key-value stores, streaming analytics, machine learning, and graph processing for large-scale data analysis and insights.
High Performance Computing
Comprehensive exploration of advanced parallel computing techniques, covering MPI, OpenMP, GPU acceleration, and applications in scientific domains. Includes hands-on exercises for practical skill development.
Programming the Machines of Tomorrow Today - Legion Programming Model
Explore Legion, a programming model for hierarchical data and computation organization, and its applications in high-productivity libraries for complex machines.
Fortran with OpenMP
Learn parallel programming in Fortran using OpenMP. Master threads, clauses, work sharing, and optimization techniques for high-performance computing applications.
CUDA Tutorials
Learn to program NVIDIA GPUs with CUDA, covering basics, kernels, memory, parallel algorithms, shared memory, and the Thrust library for efficient GPU computing.
Concurrency Without Fear in Rust - Understanding Concepts and Solutions
Entdecken Sie, wie Rust nebenläufige Programmierung sicherer und einfacher macht. Lernen Sie die Konzepte und Vorteile von "Concurrency without Fear" kennen und verstehen Sie Rusts Lösungsansätze für typische Probleme.
Computational Methods for Simulating and Analyzing Ecological Systems - Class 14
Explore computational methods for simulating and analyzing ecological systems, covering optimization, faster programming, and parallel processing techniques.
Parallel-Ready Java Code: Managing Mutation in Concurrent Systems - PhillyETE 2015
Explore parallel-ready Java code using Java 8's Collectors API for managing concurrent access to mutable collections, enabling efficient parallelism in imperative programming.
The Mutability Matrix of Pain: An Accessible Exploration of the JVM Heap - Lecture 30
Explore JVM heap, thread stacks, and concurrency in Java and Scala. Learn to compose multi-threaded code and understand the implications of immutability for efficient programming.
Parallel Programming with Thread Pools and Iterators in Rust
Learn concurrent programming techniques using ThreadPool and Iterator with rayon in Rust. Master basic building blocks for efficient thread and task management.
Bay Area Rust Meetup - Parallel Programming and Concurrent Data Structures
Explore parallel programming in Rust, covering topics like work stealing, concurrent data structures, and memory management in this Bay Area Rust Meetup session.
Parallel Programming with Failure in Rust
Explore parallel programming techniques in Rust, focusing on handling failures and static typeclass methods for robust and efficient code development.
ASYNC and AWAIT in C# - Asynchronous Programming Tutorial
Explore asynchronous programming in C# using async and await keywords. Learn to write non-blocking, scalable code for improved performance in long-running tasks and IO operations.
Vector API - SIMD Programming in Java
Explore SIMD programming in Java with the Vector API, enabling platform-agnostic, data-parallel operations for enhanced performance and efficiency in your applications.
MultiGPU Performance-Portable Solution for Array Programming Based on Kokkos
Explore a new Kokkos-based API for transparent, portable multiGPU array computation. Learn about implementation variations, autotuning, and performance comparisons with MPI+Kokkos on top supercomputers.
Pipelines and Beyond: Graph Types for ADTs with Futures
Explore a novel graph type system enabling arbitrary combinations of futures and recursive data types in parallel programming, enhancing pipelining and performance optimization techniques.
Some Things I Wish I Hadn't Seen - Challenging Concurrency Patterns in Code
Explore complex concurrency patterns in code bases, examining challenging examples that push the boundaries of theoretical understanding and practical implementation.
Disentanglement with Futures, State, and Interaction
Explore disentanglement in parallel programs with futures, examining its extension beyond fork/join parallelism and implications for functional, stateful, and non-deterministic code.
Efficient Distributed Deep Learning Using MXNet
Explore distributed deep learning with MXNet, covering practical challenges, deployment strategies, and advanced techniques for efficient large-scale machine learning implementations.
GraphSC: Parallel Secure Computation Made Easy
Parallel secure computation framework for efficient, oblivious execution of graph-based algorithms on large datasets, enabling practical big data analysis with minimal privacy leakage.
Improving CPU Isolation with Per-CPU Spinlocks - Performance Cost and Analysis
Explore CPU isolation improvements using per-CPU spinlocks. Analyze performance costs, cache coherence, and contention. Compare with current solutions and discuss strategies for optimizing time-sensitive task execution.