Complete stack of open sourced CUDA implementation
Posted: Fri Mar 30, 2012 2:28 pm
http://www.phoronix.com/scan.php?page=n ... px=MTA3OTI
Shinpei Kato to Phoronix.com;
"My research project has developed a fully open-source set of the device driver and runtime library for CUDA. We still need an NVIDIA's compiler (nvcc) to compile programs (Gallium3D is not yet ready for CUDA/OpenCL), but nothing else is required. In fact, NVIDIA has released an open-source compiler, hence all pieces of CUDA software stack are now open-source!"
Apparently gdev has its own set of extension to enable extra features using Linux kernel.
"There are many nice features included in the project. For instance, you can run CUDA programs in the operating system directly, and enhance the performance of file encryption and RAID5/6 parity encoding/decoding. You can also virtualize one GPU into several, so the time-sharing server can now install the GPU for multiple users. POSIX-based IPC (shared memory) is also enabled for multiple GPU contexts, which eliminates the overhead of moving data between the host and device back and forth. We have lots of extensions planed also, but they will come to appear with other academic publications later in this year."
- The absolute performance of Gdev is competitive to NVIDIA's proprietary software.
Shinpei Kato to Phoronix.com;
"My research project has developed a fully open-source set of the device driver and runtime library for CUDA. We still need an NVIDIA's compiler (nvcc) to compile programs (Gallium3D is not yet ready for CUDA/OpenCL), but nothing else is required. In fact, NVIDIA has released an open-source compiler, hence all pieces of CUDA software stack are now open-source!"
Apparently gdev has its own set of extension to enable extra features using Linux kernel.
"There are many nice features included in the project. For instance, you can run CUDA programs in the operating system directly, and enhance the performance of file encryption and RAID5/6 parity encoding/decoding. You can also virtualize one GPU into several, so the time-sharing server can now install the GPU for multiple users. POSIX-based IPC (shared memory) is also enabled for multiple GPU contexts, which eliminates the overhead of moving data between the host and device back and forth. We have lots of extensions planed also, but they will come to appear with other academic publications later in this year."
- The absolute performance of Gdev is competitive to NVIDIA's proprietary software.