CUDA Vector Addition and GPU Parallelism Fundamentals
Summary
This tutorial introduces GPU computing through vector addition, assigning each elementwise sum to a separate CUDA thread. It explains the distinction between data and task parallelism, and contrasts GPU throughput with CPU latency using a simplified example in which many additions run concurrently. The discussion lays out the kernel, thread, block, and grid hierarchy and notes that CPU and GPU memory must be allocated and data copied between host and device.
The worked example initializes input vectors, transfers them to GPU memory, launches a kernel, copies results back, and calculates cumulative error as a correctness check. It also describes splitting host and device code into separate files and configuring a CUDA project. The example is instructional rather than performance-tested: its timing assumptions simplify real workloads, and it uses a small vector and legacy tooling. It teaches general parallel programming concepts, but does not develop a trading method or analyze market data.
Key ideas
- GPU data parallelism assigns separate elementwise operations to independent threads.
- CUDA kernels organize threads into blocks, which are arranged in grids.
- Host and device memory require explicit allocation and data transfers in this example.
- The tutorial checks the computed vector against a CPU-derived result using cumulative error.
- Its latency and throughput comparison is illustrative and omits real-world overheads.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.