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parallelism

115 articles · Page 2 of 2
Alibaba Cloud Native
Alibaba Cloud Native
Oct 8, 2019 · Cloud Native

Mastering Kubernetes Jobs, CronJobs, and DaemonSets: Concepts, YAML, and Real‑World Walkthroughs

This guide walks Kubernetes beginners through the fundamentals of Job, CronJob, and DaemonSet controllers, explaining their use‑cases, key fields such as restartPolicy, backoffLimit, parallelism, and schedule, and provides step‑by‑step YAML examples and command‑line verification to illustrate parallel execution, scheduling, and update strategies.

CronJobDaemonSetJob
0 likes · 19 min read
Mastering Kubernetes Jobs, CronJobs, and DaemonSets: Concepts, YAML, and Real‑World Walkthroughs
Architect's Tech Stack
Architect's Tech Stack
Jul 14, 2019 · Backend Development

Performance Evaluation of Java 8 Stream API: Benchmarks and Insights

This article presents a comprehensive benchmark of Java 8 Stream API on large‑scale data, comparing serial and parallel stream operations with traditional external iteration across primitive, object, and reduction workloads, and draws practical recommendations on when to use streams for optimal performance.

JVMStream APIbenchmark
0 likes · 8 min read
Performance Evaluation of Java 8 Stream API: Benchmarks and Insights
MaGe Linux Operations
MaGe Linux Operations
Jul 12, 2019 · Fundamentals

Boost Python Performance: 24 Proven Techniques to Speed Up Code

This guide presents 24 practical methods—including timing measurements, faster data structures, loop optimizations, vectorization, and parallel processing—to dramatically accelerate Python code, each illustrated with clear before‑and‑after performance screenshots.

BenchmarkingOptimizationProfiling
0 likes · 7 min read
Boost Python Performance: 24 Proven Techniques to Speed Up Code
MaGe Linux Operations
MaGe Linux Operations
Apr 1, 2019 · Backend Development

Boost Python Performance: Parallelize Tasks with ThreadPool and map

This article critiques traditional Python multithreading tutorials and demonstrates how the built‑in map function together with multiprocessing.dummy's ThreadPool can dramatically speed up I/O‑bound and CPU‑bound tasks, offering concise code examples, performance benchmarks, and a real‑world thumbnail generation case study.

MultiprocessingThreadPoolmap
0 likes · 11 min read
Boost Python Performance: Parallelize Tasks with ThreadPool and map
ITPUB
ITPUB
May 31, 2018 · Big Data

Mastering Spark on DataMagic: Fast‑Track Your Big Data Skills

This article explains Spark's role in the DataMagic platform, outlines four practical steps to quickly master Spark, details key configuration and parallelism settings, shows how to modify Spark code, and provides operational tips for cluster management and job troubleshooting.

Big DataConfigurationDataMagic
0 likes · 10 min read
Mastering Spark on DataMagic: Fast‑Track Your Big Data Skills
MaGe Linux Operations
MaGe Linux Operations
Apr 19, 2017 · Artificial Intelligence

Accelerate TensorFlow Deep Learning with GPU, Multi‑GPU, and Distributed Training

This article explains how to speed up TensorFlow deep‑learning model training by using a single GPU, configuring session parameters, assigning operations to specific devices, employing multi‑GPU parallelism, and leveraging distributed TensorFlow on Kubernetes, while also discussing synchronous versus asynchronous training modes and practical best practices.

GPU AccelerationTensorFlowdeep learning
0 likes · 11 min read
Accelerate TensorFlow Deep Learning with GPU, Multi‑GPU, and Distributed Training
Architecture Digest
Architecture Digest
Jan 24, 2017 · Artificial Intelligence

TensorFlow: Large‑Scale Machine Learning on Heterogeneous Distributed Systems – Overview and Implementation

TensorFlow is a dataflow‑based programming model for large‑scale machine learning that uses directed acyclic graphs to represent computations, supports single‑device, multi‑device, and distributed execution with sophisticated node placement, communication, fault‑tolerance, and optimization techniques, and provides tools such as TensorBoard for visualization.

Dataflow GraphDistributed ComputingTensorFlow
0 likes · 13 min read
TensorFlow: Large‑Scale Machine Learning on Heterogeneous Distributed Systems – Overview and Implementation
ITPUB
ITPUB
Nov 6, 2016 · Databases

Why Is Your SQL Server Slowing Down? Understanding and Fixing CXPACKET Waits

The article explains the CXPACKET wait type in SQL Server, shows how to identify it with diagnostic queries, illustrates its parallelism mechanism with analogies and diagrams, and provides practical steps and hardware recommendations to resolve performance slowdowns caused by this wait.

CXPACKETPerformance TuningSQL Server
0 likes · 5 min read
Why Is Your SQL Server Slowing Down? Understanding and Fixing CXPACKET Waits
21CTO
21CTO
Jun 9, 2016 · Backend Development

Mastering Web Crawlers: From a 3‑Line Script to Scalable Distributed Scrapers

This article explains what a web crawler is, shows a minimal three‑line Python example, expands it into a functional crawler, identifies common shortcomings, and presents practical solutions such as parallelism, priority queues, DNS caching, Bloom‑filter deduplication, storage choices, and inter‑process communication for building robust distributed scrapers.

Deduplicationdistributed scrapingdns cache
0 likes · 9 min read
Mastering Web Crawlers: From a 3‑Line Script to Scalable Distributed Scrapers
High Availability Architecture
High Availability Architecture
Mar 2, 2016 · Fundamentals

Understanding Concurrency: Threads, Goroutine, Actor Model, and Thread‑Pool Strategies

This article explores the fundamentals of concurrency versus parallelism, the challenges of writing correct concurrent programs, the evolution from OS threads to green threads, Goroutine scheduling, actor‑model concepts, and modern strategies such as thread pools, async callbacks, and Rust's ownership model.

GoroutineThreadsactor-model
0 likes · 20 min read
Understanding Concurrency: Threads, Goroutine, Actor Model, and Thread‑Pool Strategies
Qunar Tech Salon
Qunar Tech Salon
Jan 17, 2015 · Backend Development

Four Styles of Java Concurrency: Threads, Executors, ForkJoin, and Actors

This article compares four Java concurrency approaches—raw threads, the Executor framework, parallel streams backed by ForkJoinPool, and the Actor model—explaining their implementations, advantages, drawbacks, and typical pitfalls through concrete code examples and practical discussion.

ActorsExecutorsForkJoin
0 likes · 13 min read
Four Styles of Java Concurrency: Threads, Executors, ForkJoin, and Actors