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# Gdev: Open-Source GPGPU Runtime and Driver Software
#
# README
#
# Copyright (C) Shinpei Kato
#
# Nagoya University
# Parallel and Distributed Systems Lab (PDSL)
# http://pdsl.jp
#
# University of California, Santa Cruz
# Systems Research Lab (SRL)
# http://systems.soe.ucsc.edu
#
# All Rights Reserved.
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Gdev is a rich set of open-source GPGPU runtime and driver software.
Currently it supports NVIDIA GPUs but is also portable to other GPUs.
The supported API implementaions include:
"Gdev API": A low-level API to manage details of GPUs.
"CUDA Driver API": A low-level API adovocated by NVIDIA.
"CUDA Runtime API": A high-level API adovocated by NVIDIA.
The implementation of CUDA Driver API and CUDA Runtime API is built
on top of Gdev API. For CUDA Runtime API we make use of GPU Ocelot as
a front-end implementation. You can add your favorite high-level API
to Gdev other than CUDA Driver/Runtime APIs on top of Gdev API.
Gdev provides runtime support in both the device driver and the user-
space library. Device-driver runtime support is a unique feature of
Gdev while most existing GPGPU programming frameworks take user-space
approaches. With device-driver runtime support, Gdev allows the OS to
manage GPUs as first-class citizens and execute CUDA programs itself.
Gdev's user-space runtime support is also unique in a sense that it
is available for multiple open-source and proprietary device drivers.
The supported device drivers include:
"Nouveau": An open-source driver developed by the Linux community.
"PSCNV": An open-source driver developed by PathScale.
"NVRM": A proprietary binary driver provided by NVIDIA.
To summarize, Gdev offers the following advantages:
- You have open-source access to GPGPU runtime and driver software.
- You can execute CUDA in the OS using loabable kernel modules.
- You can investigate GPU resource management in research.
- You can enhance OS and user-space runtime support capabilities.
- You can compare device drivers performance under the same runtime.
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# How to download, install, and use Gdev
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You can choose one of the following for what driver to be used:
1. Do you want to use runtime support in the OS?
-> See docs/README.gdev
2. Do you want to use user-space runtime with Nouveau?
-> See docs/README.nouveau
3. Do you want to use user-space runtime with PSCNV?
-> See docs/README.pscnv
4. Do you want to use user-space runtime with NVRM (NVIDIA Driver)?
-> See docs/README.nvrm
Once the driver is successfully installed, you can install high-level API:
1. Do you want to use CUDA?
-> See docs/README.cuda
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# The publication of the Gdev project
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S. Kato, M. McThrow, C. Maltzahn, and S. Brandt. "Gdev: First-Class
GPU Resource Management in the Operating System", In Proceedings of
the 2012 USENIX Annual Technical Conference (USENIX ATC'12), 2012.
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# Related research papers
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Y. Abe, H. Sasaki, M. Peres, K. Inoue, K. Murakami, and S. Kato.
"Power and Performance Analysis of GPU-Accelerated Systems", In
Proceedings of the 5th UESNIX Workshop on Power-Aware Computing and
Systems (HotPower'12) , 2012.
S. Kato. "Implementing Open-Source CUDA Runtime", In Proceedings of
the 54th Programming Symposium, Jan, 2013.
S. Kato, J. Aumiller, and S. Brandt. "Zero-Copy I/O Processing for
Low-Latency GPU Computing", In Proceedings of the 4th ACM/IEEE
International Conference on Cyber-Physical Systems (ICCPS'13), 2013.
Y. Fujii, T. Azumi, N. Nishio, and S. Kato. "Exploring Microcontrollers
in GPUs", In Proceedings of the 4th Asia-Pacific Workshop on Systems,
(APSys'13), 2013.
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# Reclocking the GPU
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NVIDIA's graphics cards are set very low clocks by default. To get
performance, you need to reclock your card at the maximum level.
How? Be root first, and then echo 3 to the following file:
echo 3 > /sys/class/drm/card0/device/performance_level
You can downclock your card by echoing 0 to the same file, i.e.,
echo 0 > /sys/class/drm/card0/device/performance_level
There are middleground levels 1 and 2, too. Note that Reclocking
is not completely supported by the open-source solution yet.
There are still some performance levels missing, and hence you may
not get as high performance as the blob. If you really need the same
level of performance as the blob, you can run some long-running
CUDA program with the blob, and do kexec -f your kernel before the
program is finished. Then the clock remains at the maximum level.
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# Benchmarks and Applications
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Today many CUDA programs are written using CUDA Runtime API. If you
want to test CUDA Driver API, try the following benchmarks and apps.
git@github.com:shinpei0208/gdev-app.git
git@github.com:shinpei0208/gdev-bench.git
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# Contributors
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Yuki ABE, Kyushu University
Jason AUMILLER, University of California at Santa Cruz
Takuya AZUMI, Ritsumeikan University
Masato EDAHIRO, Nagoya University
Yusuke FUJII, Ritsumeikan University
Tsuyoshi HAMADA, Nagasaki University
Masaki IWATA, AXE Inc.
Shinpei KATO, Nagoya University (Maintainer)
Marcin KOSCIELNICKI, University of Warsaw
Michael MCTHROW, University of California at Santa Cruz
Martin PERES, University of Bordeaux
Hiroshi SASAKI, Kyushu University
Yusuke SUZUKI, Keio University
Kaibo WANG, Ohio State University
Hiroshi YAMADA, Tokyo University of Agriculture and Technology