Large Language Models (LLMs) possess advanced language understanding, enabling a shift in application development where ai agents communicate with LLMs through natural language prompts to collaboratively complete tasks. . Apps like Microsoft Teams and Google Meet use LLM to summarize meetings, while search engines like Google and Bing enhance their capabilities with chat features. These LLM-based applications often require multiple API calls, creating complex workflows. Current API designs for LLM services are request-centric and lack application-level information, resulting in suboptimal performance.
The model serving field has seen significant advancements with systems like Clipper, TensorFlow Serving, and AlpaServe addressing deep learning deployment challenges. These systems focus on batch processing, caching, and scheduling, but often overlook the unique needs of LLMs. Orca and vLLM improve batching and memory utilization for LLM requests. Parrot improves LLM service by analyzing data flow at the application level and optimizing end-to-end performance. LLM orchestration frameworks such as LangChain and Semantic Kernel simplify the management of LLM applications. Parrot integrates with these frameworks and uses semantic variables for optimization. Parrot also uses DAG information to optimize LLM applications, emphasizing notice structure and request dependencies.
Researchers from Shanghai Jiao Tong University and Microsoft Research proposed Parrot, an LLM service system designed to treat LLM applications as first-class citizens, retaining application-level information through the use of semantic variables. A semantic variable is a region of text in a message with a specific semantic purpose, such as task instructions or input, and connects multiple LLM requests. By exposing prompt structures and request correlations, Parrot enables data flow analysis, optimizing end-to-end performance. Parrot's unified abstraction facilitates joint optimizations, improving scheduleability, latency hiding, and deduplication.
Parrot treats LLM requests as semantic functions implemented in natural language, executed by LLM. Semantic variables, defined as input or output placeholders in requests, maintain the structure of the request for cross-request analysis. In multi-agent applications such as MetaGPT, semantic functions such as WritePythonCode and WriteTestCode use semantic variables to connect and sequence tasks. Parrot's asynchronous design allows requests to be sent and retrieved separately, facilitating just-in-time relationship analysis. Performance criteria can be noted for each variable, optimizing and scheduling based on end-to-end requirements such as latency or throughput.
Parrot's evaluation of both production and open source LLM-based applications reveals significant improvements, achieving up to 11.7x speedup and 12x performance compared to state-of-the-art solutions. These applications require numerous LLM calls, resulting in high user-perceived latency. Treating requests individually can double end-to-end latency, but Parrot's batch approach eliminates this overhead. By scheduling consecutive requests together, Parrot directly sends outputs from one step to the next, avoiding network and queuing delays.
This study introduces Parrot, which optimizes the end-to-end performance of LLM applications by treating them as first-class citizens rather than focusing solely on individual applications. It introduces the semantic variable, an abstraction that reveals dependencies and commonalities between LLM requests, creating new optimization opportunities. The evaluation shows that Parrot can improve LLM-based applications by up to 11.7 times. This approach opens new research directions to improve scheduling features, such as ensuring end-to-end performance fairness in LLM applications.
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Asjad is an internal consultant at Marktechpost. He is pursuing B.tech in Mechanical Engineering at Indian Institute of technology, Kharagpur. Asjad is a machine learning and deep learning enthusiast who is always researching applications of machine learning in healthcare.
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