adds prereqs for the build and testing on CUDA environment 🦆

[bmorphism]

we can use this as a foundation for our build environment by swapping out maxwell_scudnn_winograd_128x128_ldg1_ldg4_tile148t_nt for a bunch of kernels we prepare based on BitNet

for now modal run trimodal.py will also attempt to answer the contextual questions for this task in a self-referrential, one can say Gödelian, metawank.

cheers! 🦆

[bmorphism]

003

    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) leverage (compute unified device architecture, parallel computation capabilities, optimization of calculations) to optimize (single-bit large language model inference)?",
    "Explain the process of quantizing neural network weights to single-bit in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation).",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) achieve lossless inference with 1.58-bit models on (graphics processing units, specialized parallel processors, hardware for handling complex computations)?",
    "Describe the custom (compute unified device architecture, parallel computation capabilities, optimization of calculations) kernels used in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) for efficient matrix operations.",
    "What role does kernel fusion play in improving (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s performance on (compute unified device architecture, parallel computation capabilities, optimization of calculations) architectures?",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) manage (graphics processing unit memory, high-speed memory for GPUs, volatile storage for computations) allocation and deallocation efficiently?",
    "Explain the implementation of rematerialization strategies in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) to optimize memory usage.",
    "How are integer mapping functions utilized in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) for vectorizable operations?",
    "Discuss methods (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) uses to prevent race conditions in multithreaded (compute unified device architecture, parallel computation capabilities, optimization of calculations) environments.",
    "How is data shuffling implemented in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) to enhance computational efficiency?",
    "Explain how kernelized operations are designed and utilized within (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation).",
    "Describe the self-referential unit testing framework for (compute unified device architecture, parallel computation capabilities, optimization of calculations) kernels in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation).",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) handle error checking and debugging in complex (compute unified device architecture, parallel computation capabilities, optimization of calculations) kernels?",
    "What strategies does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) employ for load balancing across (compute unified device architecture) threads and blocks?",
    "Explain the role of warp-level programming in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) optimizations.",
    "How are bitwise operations implemented for single-bit model inference in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)?",
    "Discuss the challenges of implementing single-bit large language models on (compute unified device architecture) and how (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) addresses them.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) utilize shared memory to enhance performance of (compute unified device architecture) kernels?",
    "Explain the use of template metaprogramming in optimizing (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) code.",
    "How is thread divergence minimized in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) implementations?",
    "Describe how (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) tests (compute unified device architecture) kernels for correctness and performance.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) manage synchronization between threads and blocks in (compute unified device architecture)?",
    "Explain the importance of memory coalescing in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) code.",
    "How are reduction operations efficiently implemented in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) using (compute unified device architecture)?",
    "Discuss the use of (compute unified device architecture) streams in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) for concurrent kernel execution.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) implement kernel fusion to reduce memory bandwidth requirements?",
    "Explain how occupancy calculators are used to optimize (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) kernels.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) ensure numerical stability with single-bit quantization?",
    "Describe the approach for performance testing of (compute unified device architecture) kernels in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation).",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) handle graphics processing unit memory fragmentation over prolonged computations?",
    "Explain the use of dynamic parallelism in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) code.",
    "How are custom data types like packed ternary representations defined and used in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)?",
    "Discuss the implementation and testing of the custom memory manager in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation).",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) optimize graphics processing unit utilization for large-scale language models?",
    "Explain the profiling techniques used in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) for (compute unified device architecture) kernel optimization.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) integrate with deep learning frameworks for model deployment?",
    "Describe the process and challenges of compiling (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) with (NVIDIA CUDA Compiler, compiler for CUDA code, tool for compiling GPU code).",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) handle compatibility across different (NVIDIA graphics processing unit architectures, versions of GPU hardware, generations of NVIDIA GPUs)?",
    "Explain the use of inline parallel thread execution assembly for kernel optimization in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation).",
    "How are atomic operations employed in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) to maintain data integrity?",
    "Discuss latency reduction strategies in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s inference pipeline.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) implement single-bit gradient computation for potential training support?",
    "Explain methods for maximizing memory bandwidth utilization in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation).",
    "How are large matrix operations optimized in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) using (compute unified device architecture)?",
    "Describe the testing framework for verifying correctness of (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) code.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) utilize mixed-precision computations to improve performance?",
    "Discuss handling of overflows and underflows in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s quantized computations.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) handle model serialization and deserialization efficiently?",
    "Explain the impact of kernel launch parameters on (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s performance.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) maintain compatibility with different (compute unified device architecture toolkit versions, versions of CUDA software, updates of CUDA tools)?",
    "Describe methods for detecting memory leaks in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s graphics processing unit code.",
    "What techniques does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) use to optimize instruction-level parallelism?",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) handle batch processing and data loading efficiently?",
    "Explain the process for integrating and testing new (compute unified device architecture) kernels in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation).",
    "How are unit tests structured in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) to cover (compute unified device architecture) kernel functionality?",
    "Discuss challenges and solutions for debugging (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) code.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) implement logging and error reporting for (compute unified device architecture) operations?",
    "Explain potential extensions of (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) for future graphics processing unit architectures.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) manage and test multi-graphics processing unit configurations effectively?",
    "Describe the continuous integration pipeline for (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) codebase.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) mitigate precision loss due to aggressive quantization?",
    "Discuss future optimization paths for (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) using advanced (compute unified device architecture) features.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) ensure reproducible results across different hardware setups?",
    "Explain benchmarking methods used to compare (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) with other inference frameworks.",
    "How are code documentation and comments maintained in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s (compute unified device architecture) code?",
    "What methods are used in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) to detect race conditions and deadlocks?",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) improve code coverage through unit tests for (compute unified device architecture) code?",
    "Discuss the use of (compute unified device architecture) Graphs in potential optimizations for (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation).",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s implementation guide improvements in (Triton-based large language models, high-performance LLMs using Triton, optimized LLMs with Triton backend)?",
    "Explain the role of self-referential unit tests in enhancing (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s reliability.",
    "How does (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) integrate self-testing mechanisms within its codebase?",
    "Discuss the importance of self-referential tests for optimizing (Triton-based large language models, high-performance LLMs using Triton, optimized LLMs with Triton backend).",
    "How can insights from (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation)'s code inform the development of more efficient (Triton-based large language models)?",
    "Explain how unit tests in (large language model implementation for accelerated execution, ternary quantization of weights, high-performance implementation) contribute to the overall system robustness.",

000

001

to the best of our ability, prepare a step-by-step graph plan for answering these questions using our codebases and provide this as an output with codebase paths as well as reasons and counterfactual justifications for why this way and not the other, then produce this plan using this:

002

now rewrite these 69 questions in such a way that the things that are referenced by name using the codebase understanding of those names can be replaced by a 3-tuple concept tesseract (similar to Lewis Carrol word ladders) -- e.g. BitNet => (llama.cpp, ternary weights, fast implementation) => (llm implementaion for accelerated execution, ternary weights, fast implementation) until any names are recursively resolved to their maximally synergistic 3-tuple of generic names fully and UNIQUELY for that triplet describing what the structure behind the name does and then replace every instance of that name in these questions with this 3-tuple

goals and next steps

• make CUDA build go AND get to operational semantics of CUDA runtime (CUBLAS? cuDNN!) running our trits