cuda streams

**CUDA streams** is the **independent command queues that allow overlapping kernel execution and memory operations** - they enable concurrency across compute and transfer tasks when dependency and hardware conditions permit. **What Is CUDA streams?** - **Definition**: Ordered sequence of GPU operations where commands in different streams may run concurrently. - **Default Behavior**: Operations in the same stream execute in issue order with implicit intra-stream dependence. - **Concurrency Use**: Separate streams can overlap kernels, host-device copies, and communication operations. - **Synchronization Controls**: Events and stream waits provide explicit dependency management across streams. **Why CUDA streams Matters** - **Latency Hiding**: Overlapping data transfer and compute improves effective pipeline throughput. - **Resource Utilization**: Concurrent stream execution can reduce idle periods on both GPU and CPU. - **Pipeline Design**: Streams are fundamental for multi-stage prefetch and execution architectures. - **Scalability**: Large training systems rely on stream-level overlap for communication and compute efficiency. - **Operational Control**: Stream events provide precise instrumentation for performance diagnostics. **How It Is Used in Practice** - **Work Partitioning**: Assign independent kernels and memcpy operations to dedicated streams. - **Dependency Graph**: Use cudaEvent synchronization instead of global barriers when possible. - **Profiler Validation**: Confirm expected overlap and identify serialization caused by hidden dependencies. CUDA streams are **a core concurrency mechanism for high-throughput GPU pipelines** - deliberate stream design unlocks overlap that significantly improves end-to-end performance.