Code Generation is the process of automatically producing code from a higher-level description, model, or specification, often using AI or rule-based systems to streamline development and reduce manual coding effort.

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LLM-based Distributed Code Generation and Cost-Efficient Execution in the Cloud

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The advancement of Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), is reshaping the software industry by automating code generation. Many LLM-driven distributed processing systems rely on serial code generation constrained by predefined libraries, limiting flexibility and adaptability. While some approaches enhance performance through parallel execution or optimize edge-cloud distributed processing for specific domains, they often overlook the cost implications of deployment, restricting scalability and economic feasibility across diverse cloud environments. This paper presents DiCE-C, a system that eliminates these constraints by starting directly from a natural language query. DiCE-C dynamically identifies available tools at runtime, programmatically refines LLM prompts, and employs a stepwise approach—first generating serial code and then transforming it into distributed code. This adaptive methodology enables efficient distributed execution without dependence on specific libraries. By leveraging high-level parallelism at the Application Programming Interface (API) level and managing API execution as services within a Kubernetes-based runtime, DiCE-C reduces idle GPU time and facilitates the use of smaller, cost-effective GPU instances. Experiments with a vision-based insurance application demonstrate that DiCE-C reduces cloud operational costs by up to 72% when using smaller GPUs (A6000 and A4000 GPU machines vs. A100 GPU machine) and by 32% when using identical GPUs (A100 GPU machines). This flexible and cost-efficient approach makes DiCE-C a scalable solution for deploying LLM-generated vision applications in cloud environments.

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DiCE-M: Distributed Code Generation and Execution for Marine Applications – An Edge-Cloud Approach

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Edge computing has emerged as a transformative technology that reduces application latency, improves cost efficiency, enhances security, and enables large-scale deployment of applications across various domains. In environmental monitoring, systems such as MegaSense[49], use low-cost sensors to gather and process real-time air quality data through edge-cloud collaboration, highlighting the critical role of edge computing in enabling scalable, efficient solutions. Similarly, marine science increasingly requires real-time processing and analysis of marine data from remote, resource-constrained environments. In this paper, we extend the power of edge computing by integrating it with Generative Artificial Intelligence(GenAI),specifically large language models (LLMs), to address challenges in marine science applications. We propose DiCE-M (Distributed Code generation and Execution for Marine applications), a robust system that uses LLM to generate distributed code for marine applications and then utilizes a runtime to efficiently execute it on an edge+cloud computing infrastructure. Specifically, DiCE-M leverages edge computing to execute lightweight AI models locally on unmanned surface vehicles(USVs)while offloading complex tasks to the cloud, thus balancing computational load and enabling realtime monitoring in marine environments. We use marine litter identification as an example application to demonstrate the utility of DiCE-M. Our results show that DiCE-M reduces latency by more than 2X when marine litter is not detected and cuts cloud computing costs by more than half compared to traditional cloud-based approaches. By selectively cropping and transmitting relevant image portions, DiCE-M further improves bandwidth efficiency, making it a reliable and cost-effective solution for deploying AI-driven applications on resource-constrained USVs in dynamic marine environments.

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DiCE: Distributed Code generation and Execution

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Generative artificial intelligence (GenAI), specifically, Large Language Models (LLMs), have shown tremendous potential in automating several tasks and improving human productivity. Recent works have shown them to be quite useful in writing and summarizing text (articles, blogs, poems, stories, songs, etc.), answering questions, brainstorming ideas, and even writing code. Several LLMs have emerged specifically targeting code generation. Given a prompt, these LLMs can generate code in any desired programming language. Many tools like ChatGPT, CoPilot, CodeWhisperer, Cody, DeepSeek Coder, StarCoder, etc. are now routinely being used by software developers. However, most of the prior work in automatic code generation using LLMs is focused on obtaining “correct” and working code, and mainly runs on a single computer (serial code). In this paper, we take this to the next level, where LLMs are leveraged to generate code for execution on a distributed infrastructure. We propose a novel system called DiCE, which takes serial code as input and automatically generates distributed version of the code and efficiently executes it on a distributed setup. DiCE consists of two main components (a) LLM-based tool (Synthia) to understand dependencies in serial code and automatically generate distributed version of the code using specialized programming model and semantics, and (b) Runtime (Hermod) to understand the semantics in the distributed code and realize efficient execution on a cluster of machines (distributed infrastructure). DiCE currently focuses on visual programs synthesized by tools like ViperGPT [1] and VisReP [2] (serial code), automatically identifies higher-level task parallelism opportunities (e.g., parallel object detection), transforms the code to exploit the parallelism, and finally efficiently executes it on a cluster of machines. Through our experiments using 100 examples from the GQA dataset [3], we show that the serial codes generated by ViperGPT are successfully transformed into distributed codes which are then efficiently executed on a cluster of machines by DiCE. We note that DiCE correctly identifies opportunities for parallelism and distributes tasks on separate GPUs within the cluster. We observe an average speed-up of 2X, 2.95X, and 3.7X, and an average efficiency of 1, 0.74 and 0.48 for a cluster of 2 nodes, 4 nodes, and 8 nodes, respectively.

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