Implementing GPU Multi-Stream Capabilities to Dagger.jl

Dagger.jl currently serializes all GPU tasks onto a single stream per device, leaving independent DAG branches unable to run concurrently and GPU hardware severely underutilized. This project implements multi-stream execution for the CUDA and ROCm backends by replacing the single fixed stream with a per-device stream pool, integrating stream-aware scheduling into the task dispatch layer, and replacing the global device barrier with event-based synchronization. The expected outcome is a significant reduction in end-to-end latency, improved Streaming Multiprocessor occupancy, and meaningful overlap between memory transfers and kernel execution.