Logo des Repositoriums

Auflistung nach Schlagwort "GPU-Acceleration"

1 - 2 von 2
  • Textdokument
    Full-Scale File System Acceleration on GPU
    (Tagungsband des FG-BS Frühjahrstreffens 2024, 2024) Maucher, Peter; Kittner, Lennard; Rath,Nico; Lucka,Gregor; Werling,Lukas; Khalil,Yussuf; Gröninger,Thorsten; Bellosa,Frank
    Modern HPC and AI Computing solutions regularly use GPUs as their main source of computational power. This creates a significant imbalance for storage operations for GPU applications, as every such storage operation has to be signalled to and handled by the CPU. In GPU4FS, we propose a radical solution to this imbalance: Move the file system implementation to the application, and run the complete file system on the GPU. This requires multiple changes to the complete file system stack, from the actual storage layout up to the file system interface. Additionally, this approach frees the CPU from file system management tasks, which allows for more meaningful usage of the CPU. In our pre- liminary implementation, we show that a fully-featured file system running on GPU with minimal CPU interaction is possible, and even bandwidth-competitive depending on the underlying storage medium.
  • Textdokument
    Workload-Driven Data Placement for GPU-Accelerated Database Management Systems
    (BTW 2019 – Workshopband, 2019) Schmidt, Christopher; Uflacker, Matthias
    An increase in the memory capacity of current Graphics Processing Unit (GPU) generations and advances in multi-GPU systems enables a large unified GPU memory space to be utilized by modern coprocessor-accelerated Database Management System (DBMS). We take this as an opportunity to revisit the idea of using GPU memory as a hot cache for the DBMS. In particular, we focus on the data placement for the hot cache. Based on previous approaches and their shortcomings, we present a new workload-driven data placement for a GPU-accelerated DBMS. Lastly, we outline how we aim to implement and evaluate our proposed approach by comparing it to existing data placement approaches in future work.