The IDFlow Work Networking : DMA to DMA Buffer write throughs with caching
Frame transport speed QFT & fast transport for displays (c)RS
The IDFlow Work Networking : DMA to DMA Buffer write throughs with caching : (c)RS
Applies to Ethernet, Wifi, Network, Internal Buss, Audio, Video, CPU or processor & is the internal data flow system : The IDFlow Work Networking
With GPU to GPU & Hard Drive to Hard drive transfers direct equivalence is the primary necessity!
To have a coherent transfer between two of the equivalent systems we need a Cache for input & output..
However we can create a load on arrival eta on transfer that automates correct RAM location that is optimally sorted!
Certificate exchange IP Packets & then Device classifiers : RAM, Processing power & features, Priority, Availability, workload levels, common statistics, Routing table array.
Routing table array { passthrough hardware such as Motherboard chipset & special devices such as DMA, Busses & routing table storage & access }
IP Packet formula, Metadata {
Workload timer for OpenCL & DirectCompute workloads
Send cooky code packet on request reception or query
What we need to do first is send a quick burst of metadata; The metadata contains the application & use principle; We define preferred use & reception application!
};
DMA Cache can then be directly allocated based on size & composition of data & that memory can be directly moved to the application memory allocation, Avoiding the cache being moved internally inside the Processor or GPU IPU, NPU etcetera..
Rupert S
Requirements:
Networking & Management
https://science.n-helix.com/2023/06/tops.html
https://science.n-helix.com/2023/06/ptp.html
https://science.n-helix.com/2023/06/map.html
https://science.n-helix.com/2023/02/pm-qos.html
https://science.n-helix.com/2022/08/jit-dongle.html
https://science.n-helix.com/2022/06/jit-compiler.html
https://science.n-helix.com/2022/03/ice-ssrtp.html
https://science.n-helix.com/2022/01/ntp.html
Genuinely good JS + Python & configuration work, Windows, Linux, ARM
ML tensor + ONNX Learner libraries & files
Model examples in models folder
https://is.gd/DictionarySortJS
https://is.gd/UpscaleWinDL
https://is.gd/HPC_HIP_CUDA
https://is.gd/OpenStreamingCodecs
https://is.gd/AMDPro2024PolarisCombined
The perfect Proposal RS