HPC-GPT: Integrating Large Language Model for High-Performance Computing

Ding, Xianzhong; Chen, Le; Emani, Murali; Liao, Chunhua; Lin, Pei-Hung; Vanderbruggen, Tristan; Xie, Zhen; Cerpa, Alberto; Du, Wan

doi:10.1145/3624062.3624172

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…The implementation of hardware accelerators was shown to drastically reduce training times and improve throughput, enabling more rapid iteration and development cycles [56,57]. Custom accelerators, designed specifically for machine learning workloads, were evaluated for their ability to handle the demands of large language models [11,58,59]. The advantages of leveraging hardware acceleration include enhancing real-time inference capabilities, making large models more practical for deployment in latency-sensitive applications [60,61].…”

Section: Hardware Accelerationmentioning

confidence: 99%

Efficient Training and Inference: Techniques for Large Language Models Using Llama

Cunningham,

Archambault,

Kung

2024

Preprint

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To enhance the efficiency of language models, it would involve optimizing their training and inference processes to reduce computational demands while maintaining high performance. The research focuses on the application of model compression, quantization, and hardware acceleration techniques to the Llama model. Pruning and knowledge distillation methods effectively reduce the model size, resulting in faster training times and lower resource consumption. Quantization techniques, including 8-bit and 4-bit representations, significantly decrease memory usage and improve computational speed without substantial accuracy loss. The integration of GPUs and TPUs further accelerates the training and inference processes, demonstrating the crucial role of hardware in optimizing large-scale models. The study highlights the practical implications of those techniques, paving the way for more sustainable and scalable AI solutions.

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Section: Hardware Accelerationmentioning

confidence: 99%

Efficient Training and Inference: Techniques for Large Language Models Using Llama

Cunningham,

Archambault,

Kung

2024

Preprint

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“…Research has also looked into the integration of multimodal data processing capabilities, allowing LLMs to handle a variety of input types beyond text [45,46]. Lastly, there is a growing interest in developing lightweight models that maintain high performance while being less resource-intensive [47,48].…”

Section: Advancements In Llm Architectures For Information Retrievalmentioning

confidence: 99%

Adaptive Query Contextualization Algorithm for Enhanced Information Retrieval in Alpaca LLM

Kuo,

Huang,

Tsai

2023

Preprint

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This study focused on the development and evaluation of an Adaptive Query Contextualization Algorithm (AQCA) within the Alpaca Large Language Model (LLM) framework. The AQCA was designed to enhance the model's capability in information retrieval by employing a novel context encoding methodology that dynamically adapted to multifaceted contextual signals derived from user search history and interaction patterns. The algorithm's efficacy was rigorously tested across various metrics, including Contextual Relevance Score (CRS), Word Prediction Accuracy (WPA), Information Retrieval Fidelity (IRF), and Response Coherence Measure (RCM). Significant improvements were observed in the augmented Alpaca LLM's performance, especially in complex scenarios such as metaphorical language understanding and domain-specific knowledge integration. Challenges related to scalability, adaptability to multilingual contexts, and integration with diverse LLM architectures were identified, emphasizing the need for continued research in these areas. The study concluded that while the AQCA marked a substantial advancement in LLMs for context-aware information retrieval, it also opened avenues for future innovations focusing on technical enhancements and ethical considerations.

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“…Both approaches have been instrumental in advancing the efficiency of LLM inference [18,19]. Further research has explored the use of mixed precision training and inference, where different parts of the model operate at varying levels of precision [20][21][22]. This method has been found to balance the trade-offs between speed and accuracy, offering a practical solution for accelerating LLM inference [23].…”

Section: Introductionmentioning

confidence: 99%

Efficient Large Language Model Inference with Vectorized Floating Point Calculations

Owens,

Matthews

2024

Preprint

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The development of highly sophisticated language models has revolutionized various natural language processing tasks, demanding efficient inference processes to ensure real-time responsiveness and minimal computational resource usage. Vectorized floating point calculations present a novel and significant approach to enhancing the computational efficiency of language model inference, leveraging parallel processing capabilities to achieve substantial performance improvements. This article details the implementation of vectorized floating point calculations within GPT-Neo, demonstrating a notable 12\% increase in inference speed through comprehensive benchmarks and datasets. The evaluation highlights the optimized model's ability to reduce inference time, increase computational throughput, and lower memory usage and energy consumption without compromising accuracy. The findings reveal the potential of vectorized operations to enhance the scalability and operational efficiency of advanced language models, paving the way for more responsive and resource-efficient AI applications across diverse deployment scenarios.

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HPC-GPT: Integrating Large Language Model for High-Performance Computing

Cited by 10 publications

References 30 publications

Efficient Training and Inference: Techniques for Large Language Models Using Llama

Efficient Training and Inference: Techniques for Large Language Models Using Llama

Adaptive Query Contextualization Algorithm for Enhanced Information Retrieval in Alpaca LLM

Efficient Large Language Model Inference with Vectorized Floating Point Calculations

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