VirtualMultiArray
C++ virtual-array implementation that uses all graphics cards in system as storage (with LRU cache eviction on RAM) and uses OpenCL for data transfers. (Random access: faster than HDD) (Sequential access: faster than SSD) (big objects: faster than NVMe) (by tugrul512bit)
compute-runtime
Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL™ Driver (by intel)
| VirtualMultiArray | compute-runtime | |
|---|---|---|
| 4 | 58 | |
| 14 | 1,072 | |
| - | 2.0% | |
| 3.4 | 10.0 | |
| 9 months ago | 7 days ago | |
| C++ | C++ | |
| GNU General Public License v3.0 only | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
VirtualMultiArray
Posts with mentions or reviews of VirtualMultiArray.
We have used some of these posts to build our list of alternatives
and similar projects.
- Is there some standard approach for caching file in memory?
- I need more memory than what i can get on a GPU, how can i use system memory as CUDA swap?
- Could anyone with 2-3 PCIE v3.0 / v4.0 graphics cards run this C++ virtual array benchmark on Windows or Ubuntu? My system with PCIE v2.0 16x gets 6GB/s throughput on Ubuntu but only 2GB/s on Windows.
- A C++ virtual-array that uses graphics cards as backing store, beats HDD/SSD-swap in 4kB random access performance and NVMe-swap in sequential access if you have enough pcie bandwidth.
compute-runtime
Posts with mentions or reviews of compute-runtime.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-23.
- Intel Graphics Compute Runtime for OneAPI Level Zero and OpenCL
-
Alder Lake HDR tone mapping
Well... Fuck! https://github.com/intel/compute-runtime/issues/643
-
Proxmox VE 8.0 released!
For what it's worth, I was able to get IOMMU enabled and iGPU passthrough working for Plex on an Ubuntu 22.04 LXC container with a fresh install of Proxmox 8.0.3 (kenrel 6.2.16-3-pve) on an Intel i5-13400, ASRock B760M Pro RS/D4, also using Intel drivers released early today. I largely followed this guide.
-
rocm-opencl (rocm-opencl-runtime) rx 6600 xt support
For this little project unless someone chimes in with experience or can point to regarding the RX 6600 XT / 6650 XT / perhaps 7600 I might go with https://github.com/intel/compute-runtime . (They don't have perfect documentation re supported gpus either, the readme table doesn't list DG2, you need to go to releases to see that) . Phoronix reported it as pretty stable when they tested it with kernel 6.2 in March.
-
Vladmandic Stable Diffusion added Intel ARC GPU support on Linux
Update: I was able to fix my issue. I'm using Ubuntu 22.04.2 LTS and have the newest available kernel, 6.3.1. Installing the drivers via apt does not work, instead I needed to use https://github.com/intel/compute-runtime/releases/
- Intel Arc Driver Overhead - Just a Myth?
- How do you allocate more than 4GB of memory for OpenCL in A770 16GB?
-
Proxmox iGPU passthrough to LXC not working
Drivers: https://github.com/intel/compute-runtime/releases
-
Stable Diffusion Web UI for Intel Arc
wget https://github.com/intel/compute-runtime/releases/download/22.43.24595.30/intel-level-zero-gpu-dbgsym_1.3.24595.30_amd64.ddeb
-
Current state of Intel Arc transcoding
For OpenCL HDR/DV tone-mapping, install the extra compute-runtime if running on host.
What are some alternatives?
When comparing VirtualMultiArray and compute-runtime you can also consider the following projects:
stdgpu - stdgpu: Efficient STL-like Data Structures on the GPU
jellyfin-ffmpeg - FFmpeg for Jellyfin
kbct - Keyboard keycode mapping utility for Linux supporting layered configuration
docker-jellyfin
PMS_Updater - Shell script for updating the Plex Media Server inside the FreeNAS Plex plugin
hawck - Key-rebinding daemon for Linux (Wayland/X11/Console)
intel-graphics-compiler
docker-mods - Documentation and Examples of base container modifications
docker-plex
pve-edge-kernel - Newer Linux kernels for Proxmox VE 7
grcuda - Polyglot CUDA integration for the GraalVM
linux - Linux kernel source tree
VirtualMultiArray vs stdgpu
compute-runtime vs jellyfin-ffmpeg
compute-runtime vs kbct
compute-runtime vs docker-jellyfin
compute-runtime vs PMS_Updater
compute-runtime vs hawck
compute-runtime vs intel-graphics-compiler
compute-runtime vs docker-mods
compute-runtime vs docker-plex
compute-runtime vs pve-edge-kernel
compute-runtime vs grcuda
compute-runtime vs linux