SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
An Architectural Overview of QNX
Proceedings of the Workshop on Micro-kernels and Other Kernel Architectures
High-speed VLSI architecture for parallel Reed-Solomon decoder
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Fast Parallel Table Lookups to Accelerate Symmetric-Key Cryptography
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
Operating Systems Design and Implementation (3rd Edition)
Operating Systems Design and Implementation (3rd Edition)
Construction of a Highly Dependable Operating System
EDCC '06 Proceedings of the Sixth European Dependable Computing Conference
The design and implementation of microdrivers
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
Modern Operating Systems
Computer Organization and Design, Fourth Edition, Fourth Edition: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design)
Operating Systems: Internals and Design Principles
Operating Systems: Internals and Design Principles
GPUstore: harnessing GPU computing for storage systems in the OS kernel
Proceedings of the 5th Annual International Systems and Storage Conference
| Hi-index | 0.00 |
Coding tasks, such as encryption of data or the generation of failure-tolerant codes, belong to the most computationaly expensive tasks inside the Linux kernel. Their integration into the kernel enables the user to transparently access these functionalities, encrypted hard disks can be used in the same way as unencrypted ones. Nevertheless, Linux as a monolithic kernel is not prepared to support these expensive tasks by accessing modern hardware accelerators, like graphics processing units (GPUs), as the corresponding accelerator libraries, like the CUDA-API for NVIDIA GPUs, only offer user-space APIs. Linux is often used in conjunction with parallel file systems in high performance cluster environments and the tremendous storage growth in these environments leads to the requirement of multi-error correcting codes. Parallel file systems, which often run on a storage cluster, are required to store the calculated results without huge waiting times. Whereas the frontend of such a storage cluster can be build with standard PCs, it is in contrast nearly impossible to build a capable RAID backend with end user hardware up to now. This work investigated the potential of graphic cards for such coding applications like RAID in the Linux kernel. For this purpose, a special microdriver concept (Barracuda) has been designed that can be integrated into Linux without changing kernel APIs. For the investigation of the performance of this concept, the Linux RAID 6-system and the applied Reed-Solomon code have been exemplary extended and studied. The resulting measurements outline opportunities and limitations of our microdriver concept. On the one hand, the concept achieves a speed-up of 72 for complex, 8-failure correcting codes, while no additional speed-up can be generated for simpler, 2-error correcting codes. An example application for Barracuda could therefore be replacement of expensive RAID systems in cluster storage environments.