Measure BERT model performance using WASM and WebGPU
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WebGPU Embedding Benchmark is a tool designed to measure the performance of BERT embedding models using WebGPU and WebAssembly (WASM). It provides a platform to evaluate and compare the efficiency of different embeddings in machine learning applications, leveraging modern web-based technologies for accelerated computations.
• High-Performance Computing: Utilizes WebGPU for accelerated computations, enabling faster embeddings. • WebAssembly Integration: Runs BERT models compiled to WASM for efficient execution in web environments. • Multi-Platform Support: Compatible with modern browsers and WebGPU-enabled devices. • Customizable Benchmarks: Allows users to define custom inputs and parameters for benchmarking. • Detailed Performance Metrics: Provides detailed reports on inference time, memory usage, and throughput. • Comparison Capabilities: Enables side-by-side comparisons of different BERT embeddings.
What is WebGPU?
WebGPU is a modern graphics and compute API that allows high-performance, parallel computations on the web, similar to CUDA but for web-based applications.
How does WebGPU improve embedding performance?
WebGPU accelerates machine learning workloads by leveraging GPU hardware, enabling faster matrix multiplications and tensor operations critical for embeddings.
What is the role of WebAssembly in this benchmark?
WebAssembly (WASM) compiles BERT models into a format that runs efficiently in web browsers, enabling near-native performance for embedding computations.