Optimizing the LINPACK Algorithm for Large-Scale PCIe-Based CPU-GPU Heterogeneous Systems
Guangming Tan, Chaoyang Shui, Yinshan Wang, Xianzhi Yu, Yujin Yan
Abstract
There is a widening gap between GPU and other components (CPU, PCIe bus and communication network) in heterogeneous parallel system. The gap forces us to orchestrate cooperative execution among these components much more carefully than ever before. By taking the LINPACK benchmark as a case study, this article proposes a fine-grained pipelining algorithm on large-scale CPU-GPU heterogeneous cluster systems. First, we build an algorithmic model that reveals a new approach to GPU-centric and fine-grained pipelining algorithm design. Then, we present four model-driven pipelining algorithms that incrementally squeeze bubbles in the pipeline so that it is occupied by more useful floating-point calculations. The algorithms are implemented on both the AMD and NVIDIA GPU platforms. The finally optimized LINPACK program achieves 107 PFlops on 25, 600 GPUs (70 percent floating-point efficiency). Several insights have been drawn to suggest tradeoff of algorithm design, programming support, and architecture design.