SIMD
PulseAugur coverage of SIMD — every cluster mentioning SIMD across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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New C-based inference engine Quantr explores running LLMs without GPUs
A new experimental LLM inference engine named Quantr has been developed in C to explore the intricacies of running large language models without relying on GPUs. The project focuses on quantized model weights, GGUF form…
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New SIMD UTF-8 validator for bytestrings developed
A new SIMD UTF-8 validator has been developed, designed for efficient processing of bytestrings. The developer hopes for positive outcomes within the next four weeks.
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Hillock project replaces vector databases with SQLite and SIMD hypervectors
A new open-source project called Hillock aims to replace traditional vector databases for Retrieval-Augmented Generation (RAG) with a more efficient system. Hillock extracts relational facts into SQLite, bypassing the n…
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New Viterbi algorithm formulation boosts HSMM decoding performance on GPU
Researchers have developed a new tensor-based formulation of the Viterbi algorithm for Hidden Semi-Markov Models (HSMMs), significantly improving computational performance. This novel approach restructures the algorithm…
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Rust SIMD capabilities explored for GPU acceleration
This article explores the implementation of Rust's Single Instruction, Multiple Data (SIMD) capabilities on graphics processing units (GPUs). It delves into the technical aspects of leveraging SIMD for parallel processi…
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Developer releases SIMD Viterbi decoder in Rust; article questions FIPS 140-3 security
A developer has created a SIMD Viterbi decoder implemented in Rust, revisiting a project initially built in C. This new version leverages Rust's standard SIMD capabilities for improved performance. Separately, an articl…
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Quotient Tree Arithmetic introduced for efficient ML computations
Researchers have introduced Quotient Tree Arithmetic (QTA), a novel computational framework designed to represent values as deferred quotient pairs. This method aims to improve efficiency and accuracy in computations, p…
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ExaGEMM framework boosts CPU ML inference with low-bit GEMM optimization
Researchers have developed ExaGEMM, a framework designed to optimize low-bit General Matrix Multiply (GEMM) operations for machine learning inference on CPUs. This framework addresses the challenge of efficiently execut…
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ExaGEMM framework boosts CPU ML inference with low-bit GEMM optimization · 2 sources tracked
Researchers have developed ExaGEMM, a framework designed to optimize low-bit General Matrix Multiply (GEMM) operations for machine learning inference on conventional CPUs. This framework addresses the challenge of effic…
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Spring AI and JEP 489 enable faster, cheaper local LLM re-ranking
This article details a method for optimizing Retrieval-Augmented Generation (RAG) performance by performing local re-ranking of retrieved documents. It advocates for using Java's JEP 489 Vector API for SIMD-accelerated …