One of the reasons GPUs are regularly discussed in the same breath as AI is that AI shares the same fundamental class of problems as 3D graphics. They are both embarrassingly parallel. 

Embarrassingly parallel problems refer to computational tasks that: 

Exhibit independence: Subtasks do not rely on intermediate results from other tasks.

Require minimal interaction: Parallel tasks require little to no data exchange during execution.

Are decomposable: Processing can be split into a single set of many identical tasks

Or… A hierarchy of many tasks which also contain many subtasks.  

Problems of this type achieve significant performance improvements by utilising many processors, making them ideal for highly parallel or distributed computing platforms, like a GPU.  Common examples include: 

• 3D Rendering: Each frame or pixel can be processed independently, leveraging GPUs for high efficiency. 
• Monte Carlo Simulations: Used in statistical modelling and risk analysis. 
• Cryptography: Brute-force searches and password cracking. 
• Image Processing: Applying filters or resizing across large image datasets. 
• Machine Learning: Steps like random forest tree growth or CNN inference on GPUs. 

Challenges in Efficient Implementations

Despite their inherent simplicity, embarrassingly parallel problems face several challenges: 

• Over-Parallelisation: Excessive thread creation can lead to diminishing returns due to overhead. 
• Resource Management: Contention for memory and other resources can reduce efficiency. 
• Load Balancing: Uneven distribution of tasks across processors can lead to bottlenecks. 
• Hardware Limitations: Ignoring platform-specific constraints like available cores or memory bandwidth. 
• Synchronisation Overheads: While minimal, improper synchronisation can still introduce delays.

A significant challenge in addressing these issues is maintaining performance portability which is essential for ensuring workloads can efficiently run across different hardware architectures without requiring extensive modifications.  Over-optimisation can lead to a lack of portability and therefore lock-in to a specific vendor. This issue becomes more pronounced with domain specific accelerators like NPUs. Open programming APIs, like OpenCL, offer a path to achieving high performance parallel processing across different platforms.

Edge Processing Solutions for Embarrassingly Parallel Problems 

Demand for on-device graphics performance and high-performance edge AI inference, has created a need for efficient, scalable parallel processing solutions.  

Classic challenges arise from the constrained resources typical of edge devices. Limited power budgets, reduced memory, and the need for real-time performance require careful optimisation. Algorithms must be streamlined to fit within the smaller compute and memory footprints of edge processing systems. At the same time, scalability and flexibility remain essential for supporting a growing array of inference tasks across diverse hardware. 

Advancements in deep learning, such as the introduction of transformer architectures, and breakthroughs in computer vision techniques, including zero-shot learning and self-supervised models, have dramatically increased the computational complexity and shifted hardware requirements. The rapid evolution of embarrassingly parallel workload algorithms is delivering superior edge performance, but also presents a unique challenge for hardware investment.  It highlights the need for adaptive and versatile hardware that can keep pace with the rapid changes in algorithmic development. 

New models and methods often emerge at a pace that outstrips the adaptability of traditional neural processing units (NPUs), making these investments inherently high-risk. NPUs are typically optimised for specific tasks, making them highly efficient for current inference workloads but less versatile when faced with significant shifts in computational requirements, such as the rise of transformer-based models or innovative computer vision techniques.   

This misalignment underscores the importance of balancing hardware specialisation with versatility in a hardware system. In this context, versatility refers to programmability, broader workload support, and the ability to adapt to rapidly evolving algorithmic requirements. Hardware that can accommodate a diverse range of inference tasks ensures longevity and reduces the risk of obsolescence as computational demands shift.  GPUs, for example, are designed with broader programmability, allowing them to adapt to rapidly changing algorithmic trends. 

Imagination has a solid foundation in GPU design and a proven record of developing efficient, scalable hardware solutions tailored to embarrassingly parallel workloads. Our focus on innovation in areas such as efficiency, open ecosystems, advanced tooling and innovations in embarrassingly parallel processing differentiate our products while enabling developers to maximise performance and ease of use. Key technical areas of focus include: 

• Performance-efficient compute architectures for edge and embedded devices. 
• Low power parallel compute with fine grained SIMD execution and efficient memory hierarchies. 
• Minimal data transfer between processing units. 
• Specialised hardware acceleration paths to target different workloads efficiently, including support for mixed precision arithmetic. 
• Open and cross-platform API and software ecosystem 
• First class support for OpenCL, Vulkan and SYCL 
• Support for popular AI frameworks via optimised backends 
• Low-latency, real-time compute API support 
• Advanced compilation and optimisation tooling 
• Cross platform compiler toolchains 
• Developer-friendly debugging and profiling tools 

Our GPUs have been handling embarrassingly parallel workloads for generations and have many mechanisms to counteract the challenges of implementing AI efficiently. For example when it comes to thread divergence: 

• Control Flow Simplification: Imagination GPUs replace conditional statements with arithmetic alternatives where feasible to streamline execution. We also use predicated instructions execution versus branch for short sequence.  
• Coordinated Execution: We utilise primitives or APIs that enhance synchronisation and collective decision-making among execution threads, ensuring better resource utilisation. 
• Warp-Level Primitives: We employ equivalent subgroup functionalities across GPU Warps to make collective decisions efficiently. These primitives enhance synchronisation and allow execution groups to coordinate tasks, ensuring better resource utilisation and reduced inefficiencies. 

Evolving Workloads; Evolving Architectures 

Embarrassingly parallel problems highlight the importance of scalability and resource efficiency in modern computing, particularly in inference at the edge. By understanding their unique characteristics and leveraging appropriate hardware architectures, developers can harness the full potential of these tasks.

As hardware innovation slows due to physical limits, software and algorithmic improvements will play a crucial role in overcoming existing barriers and unlocking new opportunities in parallel computing.