What is parallel processing?
News – 21/07/25
Modern computing systems are facing growing demands. Applications are becoming more data-intensive, response times more critical, and workloads increasingly complex and parallel in nature. Yet, so far any commercially successful general-purpose CPUs were not originally designed to handle large-scale parallelism efficiently, especially for tasks that require high throughput and low latency across many concurrent threads.
This is where the architecture and methodology for parallel processing becomes essential.
Parallel processing: a precise definition
Parallel processing is the execution of multiple computational tasks simultaneously, often by dividing a larger problem into smaller, preferably independent sub-tasks that can run in parallel. This model enables systems to complete workloads faster and use hardware resources more efficiently.
According to the Lawrence Livermore National Laboratory, parallel processing is:
“Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem.”
This is distinct from sequential processing, where tasks are executed one after another on a single thread.
Real-world applications
Parallel processing is widely used in areas where computational scale and responsiveness are critical:
- AI/ML model training and inference - especially for large datasets or real-time decision-making
- Real-time analytics, including fraud detection, market prediction, and industrial monitoring
- High-performance cloud computing, such as server farms, hyperscalers, and edge deployments
- Scientific simulations (e.g., weather modeling, genomics, physics)
- Rendering and visualization, including 3D graphics and computer vision pipelines
Even with multi-core CPUs, traditional architectures often struggle with fine-grained parallelism, especially when synchronization overhead, cache coherence, and blocking operations are involved.
Why parallel processing matters for the future
As Moore’s Law slows down, preventing significant improvements to single thread execution speed, parallel processing has become a central strategy for advancing performance. However, scaling parallel workloads effectively on multicore systems requires not just more cores, but new architectural approaches.
Flow’s Parallel Processing Unit (PPU) is designed to address this challenge directly, by removing common bottlenecks in traditional CPU design, such as:
- Expensive context switches
- Cache coherence penalties
- Synchronization delays
- Rigid thread management
By rethinking the architecture from the ground up, Flow’s PPU aims to provide general-purpose parallel performance that scales efficiently.
Closing
As computing continues to evolve, parallel processing is no longer optional. It’s fundamental. Whether running AI models in the cloud or executing low-latency edge applications, parallelism unlocks the performance needed for modern workloads.
If you're rethinking performance, it’s time to explore what’s possible beyond conventional cores.
Contact us at info@flow-computing.com to learn more about Flow’s PPU and our approach to scalable computing.
Further reading & references
Forsell, M., Nikula, S., Roivainen, J., Leppänen, V., & Träff, J. L. (2022).
Performance and Programmability Comparison of the Thick Control Flow Architecture and Current Multicore Processors.
Journal of Supercomputing, 78(3), 3152–3183.
https://doi.org/10.1007/s11227-021-03985-0
Forsell, M., Roivainen, J., Leppänen, V., & Träff, J. L. (2023).
Preliminary Performance and Memory Access Scalability Study of Thick Control Flow Processors.
In Proceedings of IEEE NORCAS’23, October 31–November 1, 2023.
Intel. “What is Parallel Computing?”
https://www.intel.com/content/www/us/en/architecture-and-technology/parallel-computing/overview.html
Wikipedia. “Parallel computing.”
https://en.wikipedia.org/wiki/Parallel_computing
Lawrence Livermore National Laboratory (LLNL).
Introduction to Parallel Computing Tutorial.
https://hpc.llnl.gov/training/tutorials/introduction-parallel-computing