Parallel Computing

Architects' Tech Alliance

Jun 15, 2025 · Fundamentals

Master GPU Fundamentals: Architecture, Performance, and Programming Insights

This comprehensive guide covers GPU definitions, evolution, core components, architectural designs, performance metrics, programming models, deep‑learning applications, comparisons with other processors, practical use cases, optimization techniques, and future trends, providing a solid foundation for anyone interested in modern graphics and compute acceleration.

Computer ArchitectureGPUHardware

0 likes · 43 min read

Master GPU Fundamentals: Architecture, Performance, and Programming Insights

php中文网 Courses

Apr 22, 2025 · Fundamentals

Comprehensive Guide to Python Multiprocessing: Basics, IPC, Process Pools, and Best Practices

This article provides an in‑depth overview of Python’s multiprocessing module, covering its fundamentals, process creation, inter‑process communication methods such as Queue, Pipe, shared memory, process pools, synchronization techniques, and practical best‑practice guidelines for effective parallel programming.

IPCParallel ComputingPython

0 likes · 10 min read

Comprehensive Guide to Python Multiprocessing: Basics, IPC, Process Pools, and Best Practices

Tencent Technical Engineering

Mar 21, 2025 · Fundamentals

Fundamentals of GPU Architecture and Programming

The article explains GPU fundamentals—from the end of Dennard scaling and why GPUs excel in parallel throughput, through CUDA programming basics like the SAXPY kernel and SIMT versus SIMD execution, to the evolution of the SIMT stack, modern scheduling, and a three‑step core architecture design.

CUDAGPUGPU programming

0 likes · 42 min read

Fundamentals of GPU Architecture and Programming

Test Development Learning Exchange

Jan 22, 2025 · Artificial Intelligence

Comprehensive Guide to Python Data Science Libraries with Code Examples

This article presents a concise tutorial on essential Python data science libraries, covering data cleaning with Pandas, numerical analysis with NumPy and SciPy, visualization with Matplotlib and Seaborn, machine learning with scikit‑learn, NLP with NLTK and spaCy, time‑series modeling, image processing, database access, and parallel computing, each illustrated with ready‑to‑run code examples.

NLPParallel ComputingPython

0 likes · 7 min read

Comprehensive Guide to Python Data Science Libraries with Code Examples

Baidu Tech Salon

Jan 8, 2025 · Artificial Intelligence

Evolution of Video Search Ranking Architecture Toward an End‑to‑End Large‑Model Framework

The paper describes transforming a tightly coupled, multi‑stage video search ranking pipeline into a modular, end‑to‑end large‑model architecture that decouples recall, employs a graph‑engine parallel framework and elastic compute allocation, thereby boosting performance, flexibility, personalization and lowering long‑term operational costs.

Parallel Computingelastic resourcesend-to-end

0 likes · 10 min read

Evolution of Video Search Ranking Architecture Toward an End‑to‑End Large‑Model Framework

Baidu Geek Talk

Jan 8, 2025 · Artificial Intelligence

Evolution of Video Search Ranking Architecture Towards an End‑to‑End Large‑Model Framework

The article outlines how video search ranking has shifted from a tightly‑coupled multi‑stage cascade to an extensible, end‑to‑end, model‑centric framework called Rankflow, leveraging large‑model inference, decoupled recall, fine‑grained parallelism, and elastic compute allocation to boost performance, flexibility, and maintainability while paving the way for future retrieval‑augmented generation integration.

AILarge ModelsParallel Computing

0 likes · 11 min read

Evolution of Video Search Ranking Architecture Towards an End‑to‑End Large‑Model Framework

Top Architect

Nov 5, 2024 · Backend Development

Understanding ForkJoinPool: Principles, Implementation, and Performance Evaluation

This article explains the Fork/Join model and Java's ForkJoinPool, covering divide‑and‑conquer theory, task splitting, core APIs, code examples, common pitfalls, performance testing, and best‑practice recommendations for high‑concurrency computing.

ForkJoinPoolJava ConcurrencyParallel Computing

0 likes · 25 min read

Understanding ForkJoinPool: Principles, Implementation, and Performance Evaluation

DaTaobao Tech

Sep 6, 2024 · Backend Development

Go Language Coroutine Principles and GMP Model Implementation

The article examines Go's coroutine architecture and its GMP (Goroutine-Machine-Processor) model, tracing coroutine history, comparing kernel, user, and hybrid thread models, and detailing G, M, and P components, scheduling principles, work-stealing, preemption, and runtime implementation that give Go high concurrency and low latency.

CoroutinesGMP modelParallel Computing

0 likes · 37 min read

Go Language Coroutine Principles and GMP Model Implementation

Architects' Tech Alliance

May 14, 2024 · Artificial Intelligence

Why GPUs Are Essential for Modern Artificial Intelligence and How They Compare with CPUs, ASICs, and FPGAs

This article explains the pivotal role of GPUs in today’s generative AI era, describes their architecture and applications, compares them with CPUs, ASICs, and FPGAs, and offers guidance on selecting the right processor for AI workloads while also noting related reference resources.

Artificial IntelligenceGPUHardware

0 likes · 12 min read

Why GPUs Are Essential for Modern Artificial Intelligence and How They Compare with CPUs, ASICs, and FPGAs

Java Captain

Feb 26, 2024 · Fundamentals

Principles, Advantages, Challenges, and Future of Multithreaded Architecture

This article examines multithreaded architecture, explaining its core principles, highlighting efficiency and resource utilization benefits, discussing synchronization, communication, and management challenges along with mitigation strategies, and exploring its future impact on cloud computing, big data, AI, and overall system performance.

Parallel Computingconcurrencymultithreading

0 likes · 5 min read

Principles, Advantages, Challenges, and Future of Multithreaded Architecture

Test Development Learning Exchange

Dec 6, 2023 · Backend Development

Using Python multiprocessing and Celery for Parallel and Distributed Task Processing

This article introduces Python's multiprocessing module and the Celery task queue, explains their core concepts, and provides practical code examples for multi‑process parallel computation, inter‑process communication, asynchronous execution, scheduled jobs, result callbacks, retries, and distributed task orchestration.

CeleryParallel ComputingPython

0 likes · 7 min read

Using Python multiprocessing and Celery for Parallel and Distributed Task Processing

Test Development Learning Exchange

Oct 8, 2023 · Backend Development

Python Multiprocessing for Parallel Tasks and API Automation

This article explains Python's multiprocessing module, compares it with multithreading, outlines its benefits for CPU utilization and resource isolation, and provides five practical code examples demonstrating concurrent task execution, parallel calculations, data processing, API requests, and test case automation.

API testingParallel ComputingPython

0 likes · 6 min read

Python Multiprocessing for Parallel Tasks and API Automation

Test Development Learning Exchange

Aug 2, 2023 · Backend Development

Practical Examples of Python multiprocessing and Celery for Parallel and Distributed Task Processing

This article introduces Python's multiprocessing module and the Celery distributed task queue, explains their core features, and provides ten practical code examples demonstrating multi‑process parallel computation, inter‑process communication, asynchronous tasks, scheduling, retries, and distributed processing for real‑world applications.

CeleryParallel ComputingPython

0 likes · 7 min read

Practical Examples of Python multiprocessing and Celery for Parallel and Distributed Task Processing

Python Programming Learning Circle

Jun 17, 2023 · Big Data

Accelerating Python Data Preprocessing with Multiprocessing in Three Lines of Code

This article demonstrates how to use Python's concurrent.futures module to parallelize image resizing, turning a single‑process script into a multi‑core solution with just three additional lines of code, achieving up to a six‑fold speed‑up on typical CPUs.

Parallel ComputingPythonconcurrent.futures

0 likes · 7 min read

Accelerating Python Data Preprocessing with Multiprocessing in Three Lines of Code

Baidu Geek Talk

May 10, 2023 · Artificial Intelligence

Baidu's AI Infrastructure for Large-Scale LLM Training: Architecture, Challenges, and Optimization

Baidu’s AI infrastructure combines a massive InfiniBand‑linked GPU cluster, Kunlun chips, the PaddlePaddle framework, and the Wenxin model suite with 4D hybrid parallelism, elastic fault tolerance, and a two‑stage training pipeline to overcome computation, memory, and communication walls, delivering world‑leading MLPerf performance for large‑scale LLMs.

AI infrastructureGPU ClusterInfiniBand

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Baidu's AI Infrastructure for Large-Scale LLM Training: Architecture, Challenges, and Optimization

Architects' Tech Alliance

Apr 17, 2023 · Fundamentals

Overview of High‑Performance Computing (HPC): Architecture, Metrics, Cluster Management, Job Scheduling, and Parallel Programming Models

This article provides a comprehensive overview of high‑performance computing, covering system architectures, hardware components, performance metrics, network topologies, common parallel file systems, cluster management functions, mainstream job‑scheduling systems, and MPI‑based parallel programming models.

ClusterHPCHigh Performance Computing

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Overview of High‑Performance Computing (HPC): Architecture, Metrics, Cluster Management, Job Scheduling, and Parallel Programming Models

DataFunTalk

Feb 18, 2023 · Artificial Intelligence

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Optimization, and Practical Insights

This article details Du Xiaoman's development of the ATLAS automated machine learning platform, covering business scenarios, AI algorithm deployment challenges, the end‑to‑end production workflow, platform components such as annotation, data, training and deployment, as well as optimization techniques like AutoML, meta‑learning, NAS, and large‑scale parallelism, concluding with lessons learned and future directions.

AI deploymentData EngineeringHyperparameter Optimization

0 likes · 20 min read

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Optimization, and Practical Insights

Architects' Tech Alliance

Jan 27, 2023 · Artificial Intelligence

Challenges and Future Directions of GPU in AI Computing: A Comparison with TPU and FPGA

The article analyzes how GPUs, once dominant in accelerating AI workloads, now face limitations in precision, energy efficiency, and on‑chip networking, prompting a shift toward specialized accelerators like Google's TPU and FPGA solutions, while also exploring emerging GPU‑friendly scenarios such as VR/AR, cloud gaming, and military applications.

AI hardwareCloud ComputingFPGA

0 likes · 11 min read

Challenges and Future Directions of GPU in AI Computing: A Comparison with TPU and FPGA

Architects' Tech Alliance

Jan 9, 2023 · Fundamentals

GPU Overview: Principles, Use Cases, Limitations, and Market Landscape

This article explains GPU fundamentals, describing its role as a graphics‑oriented co‑processor, the reasons for using GPUs and other accelerators, the tasks they excel at and those they cannot handle, and outlines current market trends and architectural trade‑offs.

GPUHardware ArchitectureParallel Computing

0 likes · 9 min read

GPU Overview: Principles, Use Cases, Limitations, and Market Landscape

DeWu Technology

Jan 6, 2023 · Backend Development

Coding Standards and Best Practices for Backend Development

The guide defines backend coding standards and best practices, specifying clear naming conventions for classes, methods, variables and constants, enforcing consistent code style via .editorconfig, recommending unchecked exceptions for simpler error handling, promoting structured asynchronous logging with trace IDs, and outlining performance optimizations such as loop refinement, CompletableFuture concurrency, and proper resource management to prevent memory leaks.

Backend DevelopmentException HandlingParallel Computing

0 likes · 16 min read

Coding Standards and Best Practices for Backend Development