利用异步任务的GPU驻留高斯过程回归
GPU-Resident Gaussian Process Regression Leveraging Asynchronous Tasks with HPX
AI总结：
本研究通过在GPU上实现高斯过程回归，利用异步任务和HPX框架提升大规模数据集的计算效率。
摘要：
高斯过程（GPs）是一种广泛使用的回归工具，但精确求解器的三次复杂度限制了其可扩展性。为解决这一挑战，我们通过整合一个完全驻留于GPU的GP预测流水线扩展了GPRat库。GPRat是一个基于HPX的库，结合了基于任务的并行性与直观的Python API。我们使用优化的CUDA库实现了分块算法用于GP预测，从而利用大规模并行性进行线性代数运算。我们评估了最佳的CUDA流数量，并比较了我们GPU实现与现有CPU实现的性能。我们的结果表明，对于训练样本超过128的数据集，GPU实现提供了加速。我们观察到Cholesky分解本身的加速高达4.3，GP预测的加速高达4.6。此外，结合HPX与多个CUDA流使GPRat能够匹配，并在大数据集上超过cuSOLVER的性能，最高可达11个百分点。
英文摘要：
Gaussian processes (GPs) are a widely used regression tool, but the cubic complexity of exact solvers limits their scalability. To address this challenge, we extend the GPRat library by incorporating a fully GPU-resident GP prediction pipeline. GPRat is an HPX-based library that combines task-based parallelism with an intuitive Python API. We implement tiled algorithms for the GP prediction using optimized CUDA libraries, thereby exploiting massive parallelism for linear algebra operations. We evaluate the optimal number of CUDA streams and compare the performance of our GPU implementation to the existing CPU-based implementation. Our results show the GPU implementation provides speedups for datasets larger than 128 training samples. We observe speedups of up to 4.3 for the Cholesky decomposition itself and 4.6 for the GP prediction. Furthermore, combining HPX with multiple CUDA streams allows GPRat to match, and for large datasets, surpass cuSOLVER's performance by up to 11 percent.
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