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Vitaliy M., iOS Engineer

Vinnitsa, Ukraine
Last Updated: 8 Aug 2023

- 7+ years of commercial experience in IT - Experience in developing applications using Swift, Objective-C - Excellent knowledge in OOP - Experience in using Design patterns - Good logical thinking, self-learning, high level of responsibility - Responsible, hard-working, result-oriented, creative and communicable, team player - Good attention to details - Intermediate English - Availability starting from 01/08/2023

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Vladyslav V., Android Engineer

Lviv, Ukraine
Last Updated: 8 Aug 2023

- 7+ years of commercial experience in IT - Experience in developing applications using Java, Kotlin - Excellent knowledge in OOP - Experience in using Design patterns (MVP, MVVM) - Experience in using SQLite - Good logical thinking, hard-working, self-learning, result oriented - Good attention to details. - Upper-Intermediate English - Availability starting from 01/08/2023

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Max R., Senior .NET Engineer

Kharkiv, Ukraine
Last Updated: 4 Jul 2023

- 5+ years of commercial experience with C# and .NET - Experience with .NET Core and .NET Framework - Experience with MS SQL Server, MongoDB, Cosmos DB - RabbitMQ, Redis - Worked with Stripe, AWS (SNS, SQS, S3, EFS, Lambda, EC2), Azure(Storage, Queues), Hangfire - Knowledge of OOP and SOLID principles - Basics of HTML, CSS, Javascript, JQuery - Familiar with Docker and Kubernetes; - Intermediate English - Available ASAP

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Vladislav D., Middle iOS developer

Last Updated: 4 Jul 2023

- 6+ years of experience in iOS development; - In-depth knowledge of iOS SDK; - Experience working with fintech and cryptocurrency projects; - Good knowledge of the development of different mobile projects; - Experience working with Objective-C; - Deep abilities with Swift; - Good skills working with Firebase; - Experience in UI/UX implementation; - Good skills in maintenance and testing; - Upper-Intermediate English.

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Andrii K., iOS Engineer

Last Updated: 8 Aug 2023

- 7+ years of commercial experience in IT - Experience in developing applications using Swift, Objective-C - Excellent knowledge in OOP - Experience in using Design patterns - Good logical thinking, self-learning, high level of responsibility - Responsible, hard-working, result-oriented, creative and communicable, team player - Good attention to details - Advanced English - Availability starting from 31/07/2023

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Table of Contents

Soft skills of a Multithreading Developer

Soft skills are essential for a multithreading developer to effectively collaborate, communicate, and problem-solve within a team. These skills are crucial for the successful implementation of multithreaded applications. Here are the soft skills required at different levels of expertise:


  • Adaptability: Ability to quickly understand new concepts and adapt to changing requirements in a multithreading environment.
  • Teamwork: Collaboration with team members to achieve common goals and contribute to the overall success of the project.
  • Communication: Effective communication skills to express ideas, ask questions, and provide updates on progress.
  • Time Management: Efficiently managing time and prioritizing tasks to meet deadlines in a multithreading development setting.
  • Problem-solving: Analytical thinking and problem-solving abilities to identify and resolve issues related to multithreaded programming.


  • Leadership: The ability to take ownership of tasks, guide junior developers, and provide technical mentorship within a multithreading development team.
  • Conflict Resolution: Resolving conflicts and differences of opinion within the team in a constructive and collaborative manner.
  • Critical Thinking: Applying logical reasoning and critical thinking skills to analyze complex multithreaded scenarios and make informed decisions.
  • Attention to Detail: Paying close attention to details to ensure the accuracy and efficiency of multithreaded code implementation.
  • Adaptability: Demonstrating flexibility and adaptability to handle evolving project requirements and changing multithreading frameworks.
  • Problem-solving: Advanced problem-solving skills to identify and resolve complex issues in multithreaded applications.
  • Communication: Effective communication skills to convey technical concepts to both technical and non-technical stakeholders.


  • Mentorship: Mentoring junior and middle-level developers, providing technical guidance, and fostering their professional growth.
  • Strategic Thinking: The ability to think strategically and make informed decisions that align with the long-term objectives of multithreading projects.
  • Collaboration: Facilitating effective collaboration and communication among team members and stakeholders.
  • Project Management: Managing multithreading projects, including planning, resource allocation, and ensuring timely delivery of high-quality code.
  • Innovation: Identifying opportunities for innovation in multithreading development and implementing novel solutions to enhance performance and efficiency.
  • Problem-solving: Expert-level problem-solving abilities to tackle complex multithreading challenges and optimize application performance.
  • Decision-making: Making critical decisions related to multithreading architecture, design patterns, and technology stack selection.
  • Technical Expertise: Demonstrating comprehensive knowledge and expertise in multithreading concepts, algorithms, and best practices.

Expert/Team Lead

  • Strategic Leadership: Providing strategic direction and leading the multithreading development team towards achieving organizational goals.
  • Team Management: Managing and mentoring a team of developers, delegating tasks, and ensuring optimal team performance.
  • Communication: Excellent communication skills to effectively convey complex technical concepts to diverse stakeholders.
  • Influence: Influencing and inspiring team members to excel in their work and adopt best practices in multithreading development.
  • Cross-functional Collaboration: Collaborating with other teams and departments to integrate multithreading solutions into larger software ecosystems.
  • Innovation: Driving innovation and staying updated with the latest advancements in multithreading technologies and practices.
  • Problem-solving: Expert problem-solving skills to address complex multithreading issues and optimize application performance.
  • Technical Vision: Developing and articulating a technical vision for multithreading projects and aligning it with the overall business strategy.
  • Decision-making: Making critical decisions at the architectural level, considering factors such as scalability, performance, and maintainability.
  • Continuous Learning: Continuously learning and exploring new multithreading concepts, frameworks, and tools to stay at the forefront of the field.
  • Conflict Resolution: Resolving conflicts and managing disagreements within the team or with stakeholders while maintaining a positive and collaborative environment.

Cases when Multithreading does not work

  1. Dependency on a Single Resource: Multithreading may not work effectively when multiple threads are dependent on a single resource. In such cases, if one thread locks the resource, other threads have to wait, leading to reduced performance. For example, if multiple threads are trying to access a shared database, they might have to wait for one thread to finish before proceeding, resulting in decreased efficiency.
  2. Thread Synchronization Issues: Multithreading requires proper synchronization among threads to avoid race conditions and ensure data integrity. If synchronization is not correctly implemented, it can lead to unpredictable behavior and incorrect results. For instance, if multiple threads are manipulating a shared variable simultaneously without proper synchronization, it may result in data corruption or inconsistencies.
  3. Deadlocks: A deadlock occurs when two or more threads are waiting for each other to release resources, resulting in a situation where none of the threads can proceed. This can happen if there is a circular dependency among threads and resources. Deadlocks can cause the entire application to hang or become unresponsive.
  4. Insufficient Hardware Resources: Multithreading heavily relies on the underlying hardware resources, such as CPU cores and memory. If the hardware resources are limited, running multiple threads simultaneously may lead to contention and reduced performance. In such cases, it is essential to optimize the thread usage or consider alternative solutions, such as parallel processing or distributed computing.
  5. Non-Thread-Safe Libraries or Components: Not all libraries or components are designed to be thread-safe. If a non-thread-safe library or component is used in a multithreaded environment, it can result in unexpected behavior or crashes. It is crucial to ensure that all the libraries and components used in a multithreaded application are thread-safe or properly synchronized.
  6. Sequential Dependencies: Certain tasks have inherent sequential dependencies, meaning they cannot be parallelized effectively. For example, if a task relies on the output of a previous task and cannot proceed until that output is available, it limits the potential benefits of multithreading. In such cases, alternative approaches like task-based parallelism or asynchronous programming may be more suitable.

TOP 10 Tech facts and history of creation and versions about Multithreading Development

  • Fact 1: Multithreading is a programming methodology that allows concurrent execution of multiple threads within a single process. It was first introduced in 1965 by Christopher Strachey, a British computer scientist.
  • Fact 2: The concept of multithreading gained significant popularity in the 1990s with the emergence of multi-core processors. This allowed for true parallelism by running multiple threads simultaneously on separate CPU cores.
  • Fact 3: In 1996, Java became one of the first mainstream programming languages to natively support multithreading. This made it easier for developers to write concurrent programs and take advantage of the increasing power of multi-core systems.
  • Fact 4: The introduction of multithreading brought about new challenges in software development, such as race conditions and deadlocks. These issues arise when multiple threads access shared resources simultaneously, leading to unpredictable behavior.
  • Fact 5: To address the challenges of multithreaded programming, synchronization mechanisms were developed. One of the most commonly used mechanisms is the mutex, which allows threads to acquire exclusive access to shared resources.
  • Fact 6: As technology advanced, so did the capabilities of multithreading. In 2005, Intel introduced Hyper-Threading Technology, which simulated multiple logical processors within a single physical processor core, further enhancing performance.
  • Fact 7: The rise of cloud computing and distributed systems has further emphasized the importance of multithreading. Applications running on distributed environments often rely on multithreading to handle concurrent requests and maximize resource utilization.
  • Fact 8: Multithreading is not limited to traditional software development. It also plays a crucial role in the development of video game engines, where multiple threads are used to handle tasks such as graphics rendering, physics simulation, and AI processing.
  • Fact 9: Over the years, various programming languages and frameworks have adopted multithreading. Examples include C#, Python, Ruby, and the popular threading libraries in languages like C++ and Go.
  • Fact 10: The future of multithreading looks promising as technology continues to evolve. With the advent of technologies like quantum computing and edge computing, multithreading will play a vital role in harnessing the potential of these advancements.

TOP 10 Multithreading Related Technologies

  • Java

    Java is a widely used programming language known for its robustness and platform independence. It offers built-in support for multithreading, allowing developers to create concurrent applications easily. Java’s Thread class and Runnable interface enable developers to implement multithreading in their software efficiently.

  • C++

    C++ is a powerful programming language that provides extensive support for multithreading. It offers features like threads, mutexes, condition variables, and atomic operations, which allow developers to write highly concurrent and efficient software. C++’s Standard Template Library (STL) also provides containers and algorithms specifically designed for multithreaded programming.

  • Python

    Python, a popular programming language, provides several libraries and frameworks for multithreading. The threading module in the Python standard library allows developers to create and manage threads easily. Additionally, frameworks like asyncio and multiprocessing provide advanced tools for concurrent programming in Python.

  • .NET Framework

    The .NET Framework, developed by Microsoft, offers robust support for multithreading in software development. The Task Parallel Library (TPL) in .NET provides high-level abstractions for creating and managing parallel tasks. It also includes thread-safe collections and synchronization primitives to ensure safe concurrent access to shared resources.

  • Go

    Go is a programming language designed for efficient and concurrent software development. Its lightweight goroutines and channels make it easy to write highly concurrent applications. Go’s approach to concurrency simplifies the management of threads and enables efficient utilization of system resources.

  • Node.js

    Node.js, a JavaScript runtime environment, utilizes an event-driven, non-blocking I/O model that allows for efficient multithreading. It employs a single-threaded event loop, which enables handling multiple requests concurrently. Node.js also provides the cluster module, allowing developers to scale their applications across multiple cores.

  • OpenMP

    OpenMP is an API specification for parallel programming in shared-memory environments. It is widely used in C, C++, and Fortran applications to introduce multithreading. OpenMP allows developers to parallelize loops, sections, and tasks easily, making it a popular choice for optimizing software performance.

How and where is Multithreading used?

Case NameCase Description
1. Real-time video processingMultithreading development is crucial for real-time video processing applications, such as video streaming and video conferencing. By utilizing multiple threads, developers can allocate separate threads for capturing, encoding, decoding, and displaying video frames simultaneously. This parallel processing ensures smooth and uninterrupted video playback, reducing latency and improving the overall user experience.
2. GamingIn the gaming industry, multithreading development is essential for creating immersive and interactive gaming experiences. By using multithreading, game developers can assign separate threads for tasks like graphics rendering, physics simulations, AI processing, and audio playback. This parallel processing enables smooth gameplay, realistic visuals, and responsive controls, enhancing the overall gaming performance.
3. Web serversWeb servers handle multiple requests simultaneously, serving content to numerous users. Multithreading development allows web servers to process incoming requests concurrently, improving the server’s performance and responsiveness. By allocating separate threads for each request, the server can handle multiple connections and execute tasks like data retrieval, processing, and response generation efficiently.
4. Data analysis and processingIn data-intensive applications, multithreading development plays a vital role in accelerating data analysis and processing tasks. By utilizing multiple threads, developers can divide large datasets into smaller chunks and process them concurrently. This parallel processing significantly reduces the overall processing time, enabling faster data analysis, complex calculations, and real-time insights.
5. Scientific simulationsMultithreading development is widely used in scientific simulations that involve complex mathematical calculations and simulations. By leveraging multiple threads, scientists and researchers can divide the computational workload across different threads, enabling faster simulations and reducing the time required for complex calculations. This parallel processing capability enhances the efficiency and accuracy of scientific simulations.

Hard skills of a Multithreading Developer

Hard skills of a Multithreading Developer:


  • Understanding of basic multithreading concepts: Knowledge of thread creation, synchronization, and communication.
  • Experience with thread pooling: Ability to create and manage a pool of threads for efficient execution of tasks.
  • Debugging skills: Proficiency in identifying and resolving common multithreading issues like deadlock, race conditions, and thread synchronization problems.
  • Knowledge of synchronization primitives: Familiarity with mutexes, semaphores, and condition variables for thread coordination.
  • Experience with multithreading libraries: Familiarity with libraries like Java’s java.util.concurrent or C#’s System.Threading for implementing multithreaded applications.


  • Advanced multithreading concepts: Understanding of more complex topics like thread-safe data structures, thread scheduling, and thread communication mechanisms.
  • Proficiency in performance optimization: Ability to identify and resolve performance bottlenecks in multithreaded applications through techniques like load balancing, parallelization, and fine-grained locking.
  • Experience with asynchronous programming: Knowledge of async/await or similar constructs for writing efficient and responsive multithreaded code.
  • Strong debugging skills: Ability to analyze and debug complex multithreading issues using tools like thread profilers, memory profilers, and performance analyzers.
  • Knowledge of multithreading architectures: Understanding of different multithreading models like producer-consumer, reader-writer, and thread pools.
  • Experience with parallel processing frameworks: Familiarity with frameworks like OpenMP or CUDA for parallelizing computationally intensive tasks.
  • Understanding of hardware-level multithreading: Knowledge of concepts like hyper-threading and how they impact multithreaded application performance.


  • Expert level knowledge of multithreading concepts: Deep understanding of advanced topics like lock-free programming, memory models, and concurrent data structures.
  • Experience with designing and implementing highly scalable and performant multithreaded architectures.
  • Proficiency in low-level thread synchronization: Ability to work with low-level synchronization primitives like atomic operations, memory barriers, and lock-free algorithms.
  • Experience with distributed computing: Knowledge of frameworks like Apache Hadoop or Apache Spark for distributed processing of large datasets.
  • Strong problem-solving skills: Ability to solve complex multithreading problems and optimize performance through innovative algorithms and techniques.
  • Experience with multithreading in real-time systems: Familiarity with real-time operating systems and the challenges of developing multithreaded applications with strict timing requirements.
  • Knowledge of concurrency testing and debugging techniques: Understanding of tools and methodologies for testing and debugging concurrent and parallel programs.
  • Ability to mentor and lead a team of multithreading developers: Experience in guiding and mentoring junior developers in multithreading best practices and techniques.

Expert/Team Lead

  • Expertise in designing and implementing complex multithreading frameworks and libraries.
  • Experience with high-performance computing: Knowledge of techniques for maximizing performance in multithreaded applications running on clusters or supercomputers.
  • Ability to architect and optimize multithreaded applications for specific hardware architectures.
  • Extensive experience in performance tuning and profiling of multithreaded applications.
  • Expert level debugging skills: Ability to analyze and resolve complex issues in multithreaded applications using advanced debugging tools and techniques.
  • Experience with concurrent programming in multiple programming languages like C++, Java, or Python.
  • Strong leadership and communication skills: Ability to lead and collaborate with cross-functional teams, communicate effectively with stakeholders, and drive the successful delivery of multithreaded projects.
  • Knowledge of emerging trends and technologies in multithreading: Awareness of new developments in the field of multithreading and the ability to evaluate their potential impact on software development.
  • Experience with performance analysis and optimization tools: Proficiency in using tools like Intel VTune, Valgrind, or GDB for analyzing and optimizing multithreaded application performance.
  • Ability to conduct multithreading performance audits and provide recommendations for improvement.
  • Knowledge of multithreading best practices and coding standards: Ability to enforce best practices and coding standards across the team to ensure high-quality and maintainable multithreaded code.

Pros & cons of Multithreading

9 Pros of Multithreading

  • Increased Efficiency: Multithreading allows multiple tasks to run concurrently, which can greatly improve the overall efficiency of a program.
  • Faster Execution: By dividing a program into multiple threads, each thread can work on a different part of the task simultaneously, leading to faster execution times.
  • Better Resource Utilization: Multithreading enables better utilization of system resources, such as CPU cores, as multiple threads can be assigned to different cores for parallel processing.
  • Improved Responsiveness: Multithreading can enhance the responsiveness of an application by allowing it to handle multiple user requests or perform background tasks while remaining interactive.
  • Enhanced Performance on Multi-Core Systems: With the increasing prevalence of multi-core processors, multithreading can take advantage of parallel processing capabilities to achieve higher performance.
  • Task Separation and Simplified Design: Multithreading allows for the separation of complex tasks into smaller, more manageable threads, making the overall design of the program simpler and easier to understand.
  • Efficient Use of Waiting Time: In situations where a task involves waiting for external resources or I/O operations, multithreading can utilize the waiting time of one thread by allowing another thread to execute.
  • Scalability: Multithreading provides scalability to applications, as additional threads can be added to handle increased workloads without requiring significant changes to the overall architecture.
  • Concurrency Control: Multithreading allows for efficient management of shared resources, as it provides mechanisms such as locks, semaphores, and monitors to control access and avoid conflicts.

9 Cons of Multithreading

  • Complexity: Multithreading introduces additional complexity to the design and implementation of a program, as it requires careful synchronization and coordination between threads to avoid race conditions and other concurrency issues.
  • Increased Memory Usage: Each thread requires its own stack space and thread-specific data, which can lead to increased memory usage compared to single-threaded programs.
  • Difficult Debugging: Debugging multithreaded programs can be challenging, as issues like race conditions and deadlocks may only occur intermittently and are harder to reproduce and diagnose.
  • Thread Interference: When multiple threads access shared data simultaneously, there is a risk of thread interference, where one thread’s operation affects the correctness or consistency of another thread’s operation.
  • Synchronization Overhead: Synchronizing access to shared resources introduces overhead due to the need for locks or other synchronization mechanisms, which can impact performance.
  • Potential Deadlocks: Deadlocks can occur when two or more threads are waiting for resources that are held by other threads, resulting in a program freeze or unresponsiveness.
  • Increased Software Complexity: Multithreading requires careful consideration of thread safety and synchronization, which can make software development more complex and error-prone.
  • Difficulty in Load Balancing: Assigning tasks to different threads and achieving optimal load balancing can be challenging, especially in dynamic environments where workloads vary.
  • Platform Dependencies: Multithreading implementations may vary across different platforms and programming languages, leading to potential portability and compatibility issues.

What are top Multithreading instruments and tools?

  • Java Threads: Java Threads are the foundation of multithreading in Java. They were introduced in 1996 as part of the Java 1.0 release. Java Threads allow concurrent execution of multiple tasks within a single program, enabling developers to write efficient and scalable applications. They have been widely adopted and used in various domains such as server-side applications, parallel computing, and real-time systems.
  • POSIX Threads (Pthreads): POSIX Threads, or Pthreads, is a standard thread API for Unix-like operating systems. It was first specified in 1995 as part of the POSIX standard. Pthreads provide a portable and efficient way to create and manage threads in C and C++ programs. They have been extensively used in the development of system-level software and parallel applications.
  • OpenMP: OpenMP is an industry-standard API for shared-memory parallel programming. It originated in 1997 and has since become a widely adopted multithreading tool in the High-Performance Computing (HPC) community. OpenMP allows developers to parallelize their code using compiler directives, making it easier to exploit parallelism in existing applications. It is commonly used in scientific simulations, numerical computations, and data analytics.
  • Python Threads: Python Threads provide a way to achieve multithreading in Python. They are based on the underlying operating system threads and implemented using the thread module or the newer threading module. Python Threads are commonly used for concurrent execution of I/O-bound tasks, such as network programming, web scraping, and GUI applications.
  • Intel Threading Building Blocks (TBB): Intel TBB is a C++ library for parallel programming. It was first released in 2006 and has gained popularity for its ease of use and scalability. TBB provides higher-level abstractions for parallelism, such as parallel loops and parallel algorithms, making it suitable for both novice and expert developers. It is extensively used in industries such as finance, gaming, and simulation.
  • Microsoft Parallel Patterns Library (PPL): PPL is a C++ library introduced by Microsoft as part of the Visual Studio 2010 release. It provides a high-level programming model for parallelism, simplifying the development of concurrent applications. PPL includes parallel algorithms, data structures, and task-based parallelism constructs. It is widely used in Windows application development and has been integrated into the C++ standard library (starting from C++17) as the Parallelism TS.
  • Go Goroutines: Goroutines are a lightweight concurrency mechanism in the Go programming language. Go was released in 2009 and introduced goroutines as a way to achieve concurrent execution. Goroutines are managed by the Go runtime and can be thought of as independently executing functions. They are used extensively in Go programming for building scalable servers, network applications, and concurrent data processing pipelines.
  • Microsoft Asynchronous Agents Library (Agents): Agents is a C++ library developed by Microsoft for building concurrent and asynchronous applications. It was first introduced in 2011 as part of the Concurrency Runtime. Agents provide a high-level programming model for message-passing concurrency, including support for tasks, agents, and message queues. Agents are used in various domains, such as robotics, gaming, and distributed systems.

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