The easiest on-ramp to speeding up image/video processing is probably the npp library https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fdeveloper.nvidia.com%2Fnpp [nvidia.com] It has functionality and syntax similar to Intel's ipp library but uses an NVIDIA cuda-capable GPU to accelerate the operations.

If you want to dig in deeper you could explore OpenACC http://www.openacc-standard.org/ [openacc-standard.org] OpenACC is a directives based approach to accelerator programming. You comment or mark up your code with OpenACC directives that provide additional information that the compiler can use to generate parallel code.

Finally, you can learn CUDA C, or OpenCL, or CUDA Fortran, or NumbaPro, or one of the other programming languages that are supported on the GPU hardware of your choice. NVIDIA's CUDA C compiler is based on LLVM and the IR changes have been upstreamed to LLVM.org, There are several languages and projects in development that are leveraging the LLVM infrastructure to add GPU/parallel support.

[disclaimer: I work for NVIDIA, but the words above are my own.]

The easiest on-ramp to speeding up image/video processing is probably the npp library https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fdeveloper.nvidia.com%2Fnpp It has functionality and syntax similar to Intel's ipp library but uses an NVIDIA cuda-capable GPU to accelerate the operations.

If you want to dig in deeper you could explore OpenACC http://www.openacc-standard.org/ OpenACC is a directives based approach to accelerator programming. You comment or mark up your code with OpenACC directives that provide additional information that the compiler can use to generate parallel code.

Finally, you can learn CUDA C, or OpenCL, or CUDA Fortran, or NumbaPro, or one of the other programming languages that are supported on the GPU hardware of your choice. NVIDIA's CUDA C compiler is based on LLVM and the IR changes have been upstreamed to LLVM.org, There are a lot of languages and projects in development that are leveraging the LLVM infrastructure to add GPU/parallel support.

[disclaimer: I work for NVIDIA, but the words above are my own.]

It appears to be based on the linked site:

"In particular, ZFS’s advanced architecture addresses two of our key performance concerns: random I/O, and small I/O. In a large cluster environment a Lustre I/O server (OSS) can be expected to generate a random I/O workload. There will be 100’s of threads concurrently accessing different files in the back-end file system. For writes ZFS’s copy-on-write transaction model converts this random workload in to a streaming workload which is critical when using SATA disks. For small I/O, Lustre can leverage a ZIL placed on separate SSD devices to maximize performance."

The LLNL ZFS study has been pretty widely publicized in the HPC community. Lustre uses the filesystem API rather than mounting in. Until now Lustre used ext under-the-hood for data storage, so the performance improvement from ZFS is relative to ext. ext3/4 may very well outperform ZFS on a workstation or small server, but that's not the what Lustre is used for (even their test system is ~900TB).

Disclaimer: I used to work for LLNL.