Driven by the increasing requirements of high-performance computing applications, supercomputers are prone to containing more and more computing nodes. Applications running on such a large-scale computing system are likely to spawn millions of parallel processes, which usually generate a burst of I/O requests, introducing a great challenge into the metadata management of underlying parallel file systems. The traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner, which will inevitably confront with serious network and consistence problems. This work instead pursues to enhance the metadata performance in the scale-up manner. Specifically, we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in parallel. Our proposal designs a novel metadata server architecture, which employs CPU to interact with file system clients, while offloading the computing tasks about metadata into GPU. To take full advantages of the parallelism existing in GPU, we redesign the in-memory data structure for the name space of file systems. The new data structure can perfectly fit to the memory architecture of GPU, and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests concurrently. We implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal, and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50% under typical metadata operations. The superiority is strengthened further on high concurrent scenarios, e.g., the high-performance computing systems supporting millions of parallel threads.

在高性能计算应用程序不断增长的需求推动下，超级计算机易于包含越来越多的计算节点。在如此大规模的计算系统上运行的应用程序可能会产生数百万个并行进程，这些进程通常会生成I / O请求突发，这对基础并行文件系统的元数据管理提出了巨大挑战。克服这一挑战的传统方法是以横向扩展方式采用多个元数据服务器，这将不可避免地面临严重的网络和一致性问题。相反，这项工作试图以放大的方式增强元数据性能。具体来说，我们建议通过采用GPU并行处理元数据请求来提高每个单独的元数据服务器的性能。我们的建议设计了一种新颖的元数据服务器体系结构，该体系结构使用CPU与文件系统客户端进行交互，同时将有关元数据的计算任务卸载到GPU中。为了充分利用GPU中存在的并行性，我们重新设计了文件系统名称空间的内存中数据结构。新的数据结构可以完全适合GPU的内存体系结构，因此有助于利用GPU内的大量并行线程来同时服务于突发性元数据请求。我们实现了一个基于BeeGFS的原型，并进行了广泛的实验以评估我们的建议，并且实验结果表明，在典型的元数据操作下，基于GPU的解决方案比基于CPU的方案性能高出50％以上。在高并发情况下，例如，