Metal-benchmarks: Apple GPU microarchitecture

The GitHub repository titled "Metal Benchmarks" provides an in-depth analysis of the Apple GPU microarchitecture, focusing on its general-purpose GPU (GPGPU) performance. It details various aspects such as latencies for ALU assembly instructions, cache sizes, and unique instruction pipelines. The repository compares Apple silicon with AMD and Nvidia microarchitectures, highlighting performance differences. Key sections include discussions on on-chip memory specifications, floating-point operations per second for different Apple GPU models, and performance bottlenecks in ALU operations. It also presents detailed tables of instruction throughput and latency, alongside a comparison of power efficiency against competitors. Additional features discussed include the limitations of atomic operations on Apple GPUs, implications for rendering technologies like Unreal Engine 5's Nanite, and hardware acceleration for ray tracing. The repository speculates on future SIMD features and improvements in GPU architecture. It serves as a valuable resource for developers and researchers interested in the performance characteristics and architectural details of Apple GPUs, particularly for graphics and compute workloads.

- The repository analyzes Apple GPU microarchitecture and GPGPU performance.

- It compares Apple silicon with AMD and Nvidia microarchitectures.

- Key sections cover memory hierarchy, ALU bottlenecks, and power efficiency.

- It discusses limitations of atomic operations and ray tracing acceleration.

- The resource is aimed at developers and researchers in graphics and compute workloads.