ptx kernels? proprietary assembly? relation to KR

Please be reminded that KR domain points to the highest and most level
conceptual abstraction
in computer science.

It is at the opposite end of the spectrum in relation to assembly code

For the rest of us: looks like what Daniel is pointing us to is a
proprietary technology, assembly language

Question: how does it relate to open web standards in AI?
how does this relate do KR *concepts and terms for knowledge domains
how does it fit in the scoping of defining the KR domain?
discuss

Personally, this is outside my sphere of competence and interest, KR is at
the opposite end
Possibly also outside the IP  boundary *NVIDIA proprietary code

PDM

The term
*PTX kernel* i refers to a high-performance *GPU kernel explicitly
programmed in NVIDIA's Parallel Thread Execution (PTX) assembly language*
to optimize specific operations within KRL models, such as those used in
large language models (LLMs).
PTX: An Intermediate Language for GPUs
PTX is a low-level, human-readable, assembly-like intermediate
representation (IR) or virtual machine instruction set architecture (ISA)
for NVIDIA GPUs. It acts as a stable layer between high-level programming
languages (like CUDA C/C++, PyTorch, or Triton) and the proprietary,
architecture-specific machine code (SASS).

   - *Compilation Flow*: High-level CUDA code is first compiled into PTX.
   The NVIDIA driver then just-in-time (JIT) compiles the PTX into the
   specific SASS machine code for the target GPU architecture at runtime.
   - *Purpose*: This JIT compilation enables forward compatibility,
   allowing a single application binary to run on future GPU hardware that
   didn't exist when the program was compiled.

Role in Knowledge Representation Learning and AI
In modern AI and KRL, especially with the demanding workloads of large
models, performance optimization is critical. While most developers write
high-level code, some use PTX for extreme, hardware-specific optimizations.

   - *Manual Optimization*: Manually writing or modifying PTX code allows
   experts to leverage specific, cutting-edge hardware features (e.g., in
   Flash Attention implementations) that may not yet be exposed through
   higher-level programming interfaces or automatically utilized by compilers.
   - *Research and Analysis*: Researchers use PTX as the nearest documented
   layer to the actual machine code to analyze and optimize GPU performance,
   memory access, and power consumption for AI inference.
   - *AI for Kernels*: The field is seeing the emergence of using large
   language models (LMs) to generate and optimize efficient GPU kernels,
   sometimes working with PTX or SASS directly, to push performance beyond
   what is achievable with standard compilers alone.


   -

In summary, a *PTX kernel* is a GPU program at the assembly level,
providing a means for low-level control and advanced optimization crucial
for high-performance computing in KRL applications.