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What is GPGPU?

General-Purpose computing on Graphics Processing Units (GPGPU) leverages the parallel processing capabilities of GPUs to perform computations typically handled by CPUs. By utilizing GPGPU programming, tasks such as numerical simulations, data processing, and machine learning algorithms can be significantly accelerated. Expert GPGPU programmers use tools like CUDA and OpenCL to harness the massive parallelism of GPUs, delivering high-performance computing solutions. This approach is ideal for applications requiring intensive computational workloads, enabling faster processing times and improved efficiency.

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Ready to supercharge your project with GPGPU acceleration? Freelancer is the easiest way to find skilled GPGPU programmers for every budget. Post your project today and connect with experts proficient in CUDA, OpenCL, and parallel computing. Freelancer's Milestone Payment system ensures you only pay when you're 100% satisfied. Unlock high-performance computing power for your applications now.

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Hire GPGPU Programmers

Athanasios K.

5.0

213

7.3

96%

Thessaloniki, Greece

$32 USD per hour

Mostly interested and motivated in parallel computing ( OpenCL/CUDA/Multithreading etc.) on concepts around Cryptography, DSP, DIP or other algorithmic fields that can be enhanced with such techniques. I also had experience from academic years in the usage of libraries targeted for fast GPU development like Cublas/CuFFT/Thrust. In this platform I have gained experience in Web API development, mainly with Python tools and generally in other kind of web apps and automations. A big and important part of the work here is with trading automation, over the last 7 years I have worked with any kind of trading instruments in the US market Options, Futures, FX and even crypto stuff. I have mostly developed for Interactive Brokers and other platforms Other objective is to expand my experience in other software development areas. Open to the opportunity of contributing to interesting open-source projects.

GPGPU C Programming Python Windows Desktop Linux

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Kuldeep N.

4.8

893

7.8

96%

Jaipur, India

$20 USD per hour

With 15+ years of experience in Web Hosting, Cloud Infrastructure, Email Marketing Systems, DevOps & Linux Server Administration, I help businesses run high-performance, secure and scalable environments. I’ve worked with all major cloud platforms including AWS, Google Cloud, Digital Ocean, and have successfully delivered 850+ projects with excellent client satisfaction. 🌐 Core Services 🔸 Server & Cloud Management • Server Setup & Optimization (Linux, Ubuntu, CentOS, Alma Linux) • WHM/cPanel, Plesk, Webmin, CyberPanel • VPS / Dedicated / Cloud Server Management • Security Hardening & Firewall Setup (CSF, UFW) • HA-Proxy Load Balancing, Auto-Scaling • NGINX, Apache, PHP-FPM tuning • Backup, Disaster Recovery & Monitoring • SSL Installation, Fixing SSL/CURL Issues ________________________________________ 🔸 DevOps & Automation • Docker, Kubernetes, Ansible, Jenkins • CI/CD Pipelines • Cloud Infrastructure Automation • Git, GitHub Actions • Infrastructure Scaling & Optimization ________________________________________ 🔸 Video Streaming & Real-Time Platforms • BigBlueButton (BBB) Setup & Customization • Jitsi Meet • Red5, Wowza • Live Streaming Server setup (RTMP, HLS) ________________________________________ 🔸 Email Marketing Infrastructure • SMTP (Postfix, Send mail, PowerMTA) • DKIM, DMARC, SPF, rDNS Setup • MailWizz, Interspire, Acelle, Sendy, Mumara • Email Warmup, Inbox Optimization • SES, SendGrid, Mailgun Integration • Email Deliverability Fixes ________________________________________ 🔸 Windows & Office Services • Office 365 Setup & Administration • Windows Server Management • SSL Troubleshooting ________________________________________ 🔎 Digital Marketing Experience With 9+ years in Digital Marketing & SEO, I help clients increase online visibility using: • SEO (On-page, Technical, Off-page) • Google Ads Campaign Optimization • Website Audit & Traffic Growth Strategies Weekly and monthly performance reports are provided for full transparency.

GPGPU PHP Website Design Graphic Design Internet Marketing

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Alexandre K.

4.9

116

6.8

98%

Montreal, Canada

$50 USD per hour

My name is Alexandre Kharlamov and I am an experienced computer programmer working freelance full time since 2012, writing mostly in C/C++/Javascript, with a lot of experience in full-stack webdev, desktop apps and games. Desktop Apps & Games (Windows/Linux), Games, Web Dev, Arduino, Raspberry Pi. My areas of expertise: -Portable Linux/Windows software in C/C++/Javascript -Game programming: C/C++, Javascript, HTML5. I make web games from scratch, or using libraries or game engines like Bevy. -Web programming including database design with Javascript/jQuery and SQL, including UI, CSS and 3d graphics in WebGL. -Arduino/ESP32/Raspberry Pi programming. Linux is my desktop OS, but I have a Windows box, which allows me to create cross-platform desktop software.

GPGPU C Programming JavaScript Linux Game Design

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Islam E.

5.0

7.2

98%

Tukh, Egypt

$150 USD per hour

We employ science, technology, engineering, and mathematics (STEM) to develop smart, efficient, lightweight, and reliable solutions. We have more than 25 years of experience in software development using more than 30 programming/scripting language. We are experienced in: - Programming with (C/C++, Python, Java, C#, JavaScript, VBA, ... ) - Database (Oracle, SQL Server, MySQL, SQLite, PostgreSQL, MS Access, ... ) - Automation (Autodesk AutoCAD, Microsoft Office, ESRI ArcGIS, ... ) - GIS (ArcGIS, ThinkGeo, Oracle Spatial, PostGIS, GeoServer, QGIS, ... ) - Maps (Google Maps API, OSM API, Mapbox, Leaflet, ... ) - Routing (pgRouting, OSRM, ... ) - Image Processing/Machine Vision (MATLAB, OpenCV, ... ) - Computational Intelligence (Neural Networks, Evolutionary Algorithms, ... ) - Computational Geometry - Machine Learning - Data Mining - Optimization - Algorithms Design, Analysis, and Implementation - Simulation

GPGPU PHP ASP C Programming Java

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Dinesh K.

5.0

6.7

100%

Birmingham, United Kingdom

$35 USD per hour

Professional B.Sc(hons) in Computer Science, Systems,/Software Engineer and Technical Architect, with vast corporate and freelancing experience in Europe, working for top global names in consumer electronics and software. Specialist Skills with over 10+ years experience, include ============================================= ++ C++, WIndows, Linux. Embedded C/C++ ++ Porting Legacy code to modern compilers; in particular legacy MS VC++ and Borland C++ ++ UML, OOA/OOD using |MS Visio, IBM Rational tools, Enterprise ++Specialise in : Microsoft Tools, Visual Studio, VC++, Visio, Office etc; as a Developer and technical architect. ++Used Windows since the early 90s, using VC++, any Win32 API(s), MFC, Windows APIs, SQL Server etc. WinCE, windows embedded, embedded VC++ Windows Custom Credential Provider Development using VC++ and COM. I also offer soft skills such a Life Coaching, mentoring, teaching , motivation to achieve goals, and Spiritual /Divine (advanced) teachings, training and healing. Do get in touch, should you feel like benefitting from 30+ years of experience. Thanks, Success to you! Dinesh

GPGPU C Programming XML Windows Desktop Linux

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Hiten P.

5.0

5.9

94%

BANGALORE, India

$60 USD per hour

Experienced with a wide variety of systems and platforms like AIX, HPUX, Linux, AS/400, Windows, Windows API, SDK and DDK and various programming languages. Worked on large scale enterprise applications in domains like financials, healthcare, telecom and ERP supply chain.

GPGPU PHP ASP C Programming Java

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Yiannis P.

4.9

4.2

100%

Waltham, United States

$100 USD per hour

Researcher in the area of parallel systems, familiar with nearly all aspects of Parallel and Distributed Computing. Languages: C++, C, Java, Prolog Parallel Programming: OpenMP, MPI, Intel TBB, Intel Cilk++

GPGPU C Programming Python Linux Research

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Olecons Technology

4.6

5.3

100%

Nagda, India

$50 USD per hour

Your Tech Ally for Robust Solutions: Leverage 10+ years of experience in full-stack development to empower your business. We specialise in SaaS products, providing seamless technical support and developing scalable solutions.

GPGPU PHP JavaScript Python .NET

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Narges K.

5.0

5.1

94%

Tbilisi, Georgia

$20 USD per hour

⭐️ Master of Science in Artificial Intelligence ⭐️ ⭐️ Machine Learning / Deep Learning / Real-Time Trading Systems / Big Data ⭐️ ⚜️ Experienced in: ✔️ AI Trading Bots for Prediction Markets (Kalshi, Polymarket) ✔️ Real-time market-data systems using WebSocket & event-driven architectures ✔️ Trading dashboards, orderbook analytics, live telemetry & observability systems ✔️ Quant research, backtesting, threshold optimization & market analytics ✔️ Stable Diffusion, Midjourney, DALL·E, ControlNet, ComfyUI, A1111 ✔️ Transformers (BERT, ELMo, LLM workflows, AI agents) ✔️ Image Processing & Computer Vision (Object Detection, OCR, segmentation) ✔️ Chatbots & AI Automation Systems (Rasa, AI-assisted workflows) ✔️ NVIDIA TAO Toolkit, CUDA optimization & GPU pipelines ✔️ Streaming & distributed systems (Kafka, Spark, Amazon Kinesis) ✔️ ClickHouse, Grafana, ELK Stack, real-time analytics pipelines ✔️ Linux servers, VPS management, OpenVPN, Docker & infrastructure debugging ⚜️ Libraries & Frameworks: PyTorch, TensorFlow, Keras, Pandas, NumPy, Scikit-learn, OpenCV, NLTK, Matplotlib, CUDA, AWS, Azure ⚜️ Technologies: Kafka, Spark, Hadoop, Elasticsearch, Logstash, Kibana (ELK), ClickHouse, Grafana, WebSocket APIs ⚜️ Programming: Python, Matlab, Java, C/C++, PHP ⚜️ Data Visualization: Tableau, Power BI, live HTML5 Canvas dashboards ⚜️ Additional Skills: ✔️ AI-generated illustrations & media pipelines ✔️ AI video editing & video-to-video workflows ✔️ Real-time browser dashboards for trading/debugging systems ✔️ AI-assisted software architecture & operational tooling MetaTrader & MQL5 Development Experienced in developing and supporting automated trading systems for MetaTrader platforms, including custom Expert Advisors (EAs), indicators, trade-management tools, and real-time monitoring solutions. Skilled in designing event-driven trading logic, risk-management systems, execution automation, and market-data integrations for Forex and crypto trading workflows. Worked on projects involving: MQL4 / MQL5 Expert Advisors Trade execution automation Strategy optimization and backtesting Signal generation systems Risk and position management Real-time alerts and Telegram integrations Broker/API integrations Dashboard and telemetry systems VPS deployment and operational monitoring Debugging and optimization of existing EAs Strong focus on reliability, observability, low-latency execution, and maintainable architecture for live trading environments.

GPGPU Python Software Architecture CUDA Machine Learning (ML)

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How to hire a great freelance GPGPU Programmer

A GPGPU programmer is a specialist developer who writes parallel code that runs on graphics processing units to accelerate compute-heavy workloads such as scientific simulations, machine learning, image processing, and financial modeling. General-purpose GPU programming uses the massive parallelism of modern GPUs to deliver order-of-magnitude speedups over traditional CPU code, and a skilled GPGPU developer is the engineer who makes that performance accessible to your application.

What does a GPGPU programmer actually do

A GPGPU programmer ports, writes, and optimizes code that executes on GPU hardware instead of, or alongside, the CPU. Their job is to identify which parts of a workload are parallelizable, restructure algorithms to fit the SIMT execution model, manage host-device memory transfers, and squeeze maximum throughput from the underlying hardware.

Beyond raw kernel writing, a GPGPU specialist profiles existing code, finds bottlenecks, and decides whether GPU acceleration is the right answer at all. The commercial value is straightforward: workloads that take hours on CPU clusters can finish in minutes on a single GPU, which translates directly into lower infrastructure costs and faster product iterations.

Core deliverables and services

GPGPU work spans low-level kernel engineering through to integration with higher-level applications. Typical deliverables include:

CUDA kernels written in C, C++, or Fortran for NVIDIA hardware

OpenCL kernels for cross-vendor portability across NVIDIA, AMD, and Intel GPUs

HIP and ROCm code targeting AMD Instinct accelerators

SYCL and DPC++ implementations for heterogeneous compute

GPU-accelerated linear algebra using cuBLAS, cuSOLVER, or rocBLAS

Custom CUDA Thrust, CUB, or Kokkos pipelines for parallel primitives

Integration of GPU code with PyTorch, TensorFlow, or JAX through custom ops

Performance profiling and tuning with Nsight Systems, Nsight Compute, or rocprof

Multi-GPU and multi-node scaling using NCCL, MPI, and CUDA-aware communication

Memory hierarchy optimization across registers, shared memory, and global memory

Mixed-precision kernels using FP16, BF16, and Tensor Core instructions

Many engagements also include CPU-side refactoring, build system work with CMake, and containerization with Docker or Singularity for reproducible GPU environments.

Tools, frameworks, and APIs

A capable GPGPU expert is fluent in the toolchain that surrounds GPU compute, not just the kernel language itself. Expect working knowledge of:

CUDA Toolkit, including the nvcc compiler, PTX, and CUDA Graphs

OpenCL, Vulkan Compute, and Metal Performance Shaders

AMD ROCm, HIP, and the HIPIFY translation tools

OpenACC and OpenMP target offloading for directive-based acceleration

NVIDIA libraries such as cuDNN, cuFFT, cuSPARSE, and TensorRT

Numba, CuPy, PyCUDA, and RAPIDS for Python-based GPU work

Nsight, NVProf, CUDA-GDB, and Compute Sanitizer for debugging and profiling

Industries and common use cases

GPGPU programming touches almost every compute-bound field. Freelance GPU developers commonly work on:

Machine learning and deep learning training and inference acceleration

Computational fluid dynamics, molecular dynamics, and finite element simulation

Quantitative finance, Monte Carlo pricing, and risk analytics

Medical imaging, CT and MRI reconstruction, and bioinformatics pipelines

Computer vision, real-time video analytics, and image signal processing

Cryptography, blockchain mining research, and password recovery

Oil and gas seismic processing and reservoir simulation

Game engine compute, physics, and procedural generation

How to evaluate GPGPU candidates

Strong GPU programmers combine systems-level C and C++ skill with a deep understanding of parallel algorithms and hardware architecture. When reviewing profiles and portfolios, look for evidence of measured speedups, profiler screenshots, published kernels on GitHub, and contributions to GPU-accelerated open source projects. Domain experience matters: a CUDA developer who has shipped production deep learning kernels is not interchangeable with one who specializes in scientific HPC, even though the underlying skills overlap.

Useful interview questions you can copy and use:

Walk me through how you would profile a CUDA kernel that is running slower than expected. Which Nsight metrics would you look at first?

Explain the difference between global, shared, constant, and texture memory, and when you would use each.

How do warp divergence and memory coalescing affect kernel performance, and how do you mitigate them?

Describe a project where you ported a CPU algorithm to GPU. What speedup did you achieve and what was the main bottleneck?

When would you choose OpenCL or SYCL over CUDA, and what trade-offs come with that choice?

Ask for a small paid trial: a focused kernel optimization task with a clear baseline runtime is the fastest way to verify real ability.

Why hire GPGPU programmers on Freelancer.com

Freelancer.com gives you direct access to a global pool of GPU developers, from CUDA specialists with HPC backgrounds to machine learning engineers who write custom Triton and TensorRT kernels. You can post a project on Freelancer.com and receive competitive bids from freelancers across multiple time zones, compare portfolios that show real benchmark numbers, and shortlist candidates whose past work matches your domain. Clients set their own budgets, and Milestone Payments hold funds in escrow until agreed deliverables are met, which protects both sides on technically demanding work. The scale and verification systems on Freelancer.com make it practical to hire on Freelancer.com even for highly specialized GPU roles that would be hard to fill through traditional channels.

Post a project on Freelancer.com today and connect with skilled GPGPU programmers ready to accelerate your workloads on CUDA, OpenCL, HIP, and beyond.

What are the steps to hiring a freelance GPGPU programmer?

Hiring a GPU developer is faster when your brief gives candidates enough technical context to bid intelligently. GPGPU work is highly specialized, so the quality of the project description directly determines whether you attract serious specialists or generalists guessing at the scope. The three steps below walk through posting, reviewing bids, and awarding the project.

Step 1: Post your project on Freelancer.com

Your project post is the single biggest determinant of bid quality. A clear, technical brief filters for candidates whose CUDA, OpenCL, or HIP experience genuinely matches what you need, and it prevents wasted rounds of clarification. Head to the post project page , where you fill in the brief, scope, timeline, and budget before publishing the project for freelancers to bid on.

State the target hardware and architecture, for example NVIDIA A100, RTX 4090, or AMD MI250

Specify the GPU framework required: CUDA, OpenCL, HIP, SYCL, or a Python wrapper such as CuPy or Numba

Describe the workload, current CPU baseline runtime, and the speedup or throughput target

List the input and output data formats and any precision requirements such as FP32, FP16, or mixed precision

Note integration constraints, such as PyTorch custom ops, MPI multi-node scaling, or embedded deployment

Include access details for code, sample data, and your build environment

Step 2: Review bids from freelancers

Bids on a GPGPU project are short proposals, not just price quotes. Strong proposals show that the freelancer has actually read the brief, understands the parallelism opportunity, and has a credible plan for hitting your performance target. Read each bid carefully and shortlist candidates whose technical approach is concrete.

Look for a stated approach: which kernels they would write, which libraries they would reuse, and which optimizations they would attempt first

Favor bids that ask sharp clarifying questions about memory layout, data sizes, or precision

Check that the proposed timeline accounts for profiling and tuning, not just initial implementation

Look for references to Nsight, rocprof, or other profilers as part of the workflow

Review attached sample kernels, GitHub repositories, or benchmark write-ups

Use Freelancer.com chat to ask follow-up questions about specific architectures or libraries before shortlisting

Step 3: Evaluate freelancers and award the project

The final decision combines proposal quality with profile evidence. For GPGPU work, weigh consistency of past performance work over a single impressive demo, since real GPU optimization skill shows up across many engagements. Look at the full profile of each shortlisted candidate before awarding.

Portfolio depth in CUDA, OpenCL, HIP, or related GPU frameworks relevant to your stack

Star ratings and the volume of completed projects in systems, HPC, or machine learning categories

Written client reviews that mention measured speedups, code quality, and communication

Verified payment method, completion rate, and on-time delivery rate

Any visible contributions to GPU-accelerated open source libraries

Award the project and use Milestone Payments to release funds as kernels are delivered, benchmarked, and accepted, protecting your budget across the engagement

How long does a typical GPGPU project take?

Small kernel optimizations or proof-of-concept ports can be completed in a week or two, while production-grade GPU rewrites of large simulation or ML codebases often run for several months. Timeline depends on code complexity, whether unit tests exist, and how aggressive the performance targets are.

What is the difference between a CUDA developer and a GPGPU programmer?

CUDA is one specific platform for GPGPU programming, limited to NVIDIA hardware. A GPGPU programmer is the broader role and may also work in OpenCL, HIP, SYCL, or Vulkan Compute, which matters if you need vendor-neutral code or AMD and Intel GPU support.

Do I need a GPGPU freelancer or a regular C++ developer?

If your bottleneck is a parallelizable numerical workload and you want significant speedups on existing GPU hardware, you need a GPGPU specialist. A general C++ developer can write correct code but typically will not extract the memory, occupancy, and instruction-level optimizations that make GPU acceleration worth the effort.

Can I hire a GPGPU programmer for a one-off optimization?

Yes. Many clients hire GPU experts for fixed-scope work such as accelerating a single function, writing a custom CUDA op for a deep learning model, or profiling and tuning an existing kernel. A clear baseline benchmark and a target speedup make these engagements straightforward to scope.

What hardware should I plan to test on?

Match your test hardware to your production target. If you deploy on data center GPUs such as the A100 or H100, the freelancer should benchmark on the same architecture, since occupancy, Tensor Core behavior, and memory bandwidth differ substantially from consumer cards.