CASE STUDY
Accelerating Physics Simulation Using GPUs (Under NDA)
Our client was a small to medium-sized enterprise (SME) that developed engineering planning applications. The founder’s goal was to create a proof-of-concept to assess the potential speed-up that could be gained by utilising GPU technology.
The Challenge
Physics simulations are computationally expensive. While the existing CPU-based system worked, it was limited in terms of speed and scalability. The need for faster, real-time simulations had become apparent, and GPUs, with their powerful parallel processing capabilities, seemed like the perfect solution.
Proof-of-Concept Requirement
The founder’s goal was to create a proof-of-concept to assess the potential speed-up that could be gained by utilising GPU technology.
CPU Performance Limits
While the existing CPU-based system worked, it was limited in terms of speed and scalability.
Real-Time Need
The need for faster, real-time simulations had become apparent.
Image credits: Freepik.
Project Timeline
Research
Analysed literature on physics simulation and GPU acceleration to identify viable approaches.
Designed a more efficient algorithm using an approximation of brute-force methods, inspired by techniques from other domains.
Algorithm Design
GPU Implementation
Implemented core refinements on GPUs to leverage parallel processing and distribute workloads across many GPU cores.
Iterated on the algorithm over multiple cycles as requirements expanded, incorporating client feedback and adapting scope changes in an agile fashion.
Iterative Refinement
Integration Readiness
Developed utility functions and on-demand testing/tooling, and ensured compatibility across multiple OS/compiler environments.
The Solution
We started by analysing literature on physics simulation and GPU acceleration. That research led to a more efficient algorithm, refined through several iterations as requirements expanded, with most refinements implemented on GPUs to leverage parallel processing.
Algorithm Design
We designed a novel algorithm that improved on existing state-of-the-art solutions by using an approximation of brute-force simulation. The approach was inspired by techniques our engineers had encountered in other problem domains, enabling faster simulations without sacrificing too much accuracy.
GPU Implementation
We implemented key refinements on GPUs to take full advantage of high parallel processing power. By distributing computation across hundreds or thousands of GPU cores, we achieved significant performance gains compared to CPU-only execution.
Delivery & Flexibility
We delivered a family of algorithms designed to remain adaptable as the client’s strategic direction evolved. We also built utility functions alongside the core simulation algorithm to improve usability and make integration into existing systems easier.
Technical Specifications
Languages
C++, CUDA, CMake
Languages
Python, NumPy (fast prototyping and iteration)
High multi-core utilisation through effective task management,
GPU workloads fed efficiently with data
GPU workloads fed efficiently with data
Maintained compliance across different operating systems and compilers
Applied environment-specific optimisations where required by video cards / GPU setup
Applied environment-specific optimisations where required by video cards / GPU setup
The Outcome
TechnoLynx delivered a new simulation core that met and exceeded the client’s expectations. Runtime performance was excellent, demonstrating that GPUs could be applied effectively in this domain.
Key Achievements
Significant performance gains versus CPU-based simulation.
A visually appealing model of the required physical phenomena, improving the user experience (UX).
Demonstrated application of ray tracing and 3D graphics concepts to enhance the visual representation of simulation data
Built on-demand testing and utility tooling to support integration into the client’s existing system
Evaluated current and recent use cases using these tools to validate real-world applicability
Delivered a roadmap for future improvements and continued evolution as the client grows
Ready to Accelerate Your Application?
Let’s discuss how GPU acceleration and parallel computing can transform your computational workloads.
