BrrrViz provides interactive visual learning tools that let engineers and students explore GPU architecture and programming concepts through animated floorplans and simulations. The platform covers topics from basic execution models and memory coalescing to advanced ML system components like transformer architectures and multi‑GPU parallelism, turning abstract GPU concepts into observable, hands‑on experiences.
Funding
Funding not disclosed
Founders
Product
Problem
GPU programming and modern machine‑learning system design involve intricate concepts—such as warp scheduling, memory coalescing, and transformer architectures—that are difficult to grasp from static documentation alone, leading to steep learning curves for engineers and students.
Solution
BrrrViz offers an interactive visual learning platform that transforms these abstract GPU and ML concepts into manipulable, animated diagrams. Users can explore a detailed streaming‑multiprocessor floorplan, step through execution models, and experiment with optimization techniques like occupancy and kernel fusion in real time. The curriculum links foundational GPU topics directly to contemporary ML workloads, including transformers, flash attention, and multi‑GPU parallelism, enabling learners to see how low‑level hardware behavior impacts high‑level model performance. By providing watchable, interactive visualizations, the platform accelerates comprehension and reduces the time required to develop high‑performance GPU‑accelerated applications.
Target Audience
Primary users are software engineers, GPU developers, and computer‑science students who need to understand and optimize GPU code, as well as ML practitioners seeking deeper insight into the hardware underpinnings of large‑scale models.
Features
- Interactive streaming‑multiprocessor floorplan that visualizes warp execution, memory access patterns, and synchronization primitives
- Step‑through execution models with controls for pausing, rewinding, and adjusting parameters such as thread block size and occupancy
- Visual modules for GPU optimization topics (memory coalescing, bank conflicts, atomic operations, tiling, profiling) with live performance feedback
- Dedicated ML systems curriculum covering transformer architectures, flash attention, KV caches, continuous batching, speculative decoding, and multi‑GPU parallelism
- Hands‑on experiments for kernel fusion and precision trade‑offs, allowing users to observe impact on throughput and resource utilization
- Integrated learning flow that combines GPU fundamentals with modern ML workload examples, reinforcing concepts across both domains