Cloud-Native Code Translation for Portable HPC Offloading
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Solution Overview
Problem
Existing large-scale data-parallel optimization problems in scientific computing face challenges with high computational complexity, cost, resilience, and management overhead when deployed on cloud-based clusters, requiring manual re-design and re-implementing for serverless and semi-serverless approaches, which are not portable and platform-dependent.
Innovation Solution
A framework that extends high-level languages like Python or Julia to cloud services using a software abstraction layer, enabling users to offload computations to cloud-native environments with minimal code modifications, leveraging batch and serverless computing for distributed extensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If on-demand cloud clusters are used to run data-parallel optimization problems, then accessibility and ease of deployment are improved, but cost and management overhead increase significantly
Solution Approach 1:
The patent introduces a cloud-native framework that acts as an intermediary layer between traditional HPC applications and cloud infrastructure. This framework automatically manages cluster provisioning, resource allocation, and job scheduling through API integrations with cloud providers, eliminating the need for users to manually manage cluster complexity while maintaining ease of deployment.
Solution Approach 2:
The system implements self-service capabilities where the HPC framework automatically provisions compute resources, configures environments, and manages cluster lifecycles without human intervention. The framework monitors resource usage and dynamically scales infrastructure based on workload demands, reducing management overhead while preserving accessibility.
2Adaptability or versatility
If traditional MPI-based applications are run on cloud clusters, then existing applications can be executed, but resilience and elasticity are not fully utilized
Solution Approach 1:
The patent transforms static MPI applications into dynamic cloud-native applications by introducing a runtime system that automatically adjusts resource allocation based on workload demands. The framework enables elastic scaling by dynamically provisioning or deprovisioning compute nodes during execution, while maintaining application resilience through automatic failure detection and recovery mechanisms.
Solution Approach 2:
The system changes the operational parameters of HPC applications by replacing fixed cluster configurations with dynamic resource allocation. The framework modifies how applications interact with infrastructure by introducing cloud-native abstractions that enable automatic scaling, failover, and resource optimization, thereby achieving both elasticity and resilience simultaneously.
3Loss of energy
If serverless and semi-serverless approaches are implemented, then cost savings and resource utilization improve, but applications require fundamental re-design and re-implementation
Solution Approach 1:
The patent creates a universal framework that supports multiple deployment modes including serverless, semi-serverless, and traditional cluster configurations. This multi-functional system allows applications to be executed across different cloud service models without fundamental re-design, while still achieving cost savings through automatic resource optimization and serverless integration where applicable.
Solution Approach 2:
The framework introduces an intermediary translation layer that converts traditional HPC application interfaces into cloud-native serverless-compatible formats. This intermediary handles the complexity of re-implementation by automatically managing function packaging, event-driven execution, and state management, thereby reducing implementation complexity while maintaining cost efficiency.
4Ease of operation
If cloud-native frameworks are used to translate user API calls to SDK calls, then code portability and ease of operation improve, but translation and processing overhead increase
Solution Approach 1:
The patent implements preliminary action by pre-compiling and caching translation rules for common API patterns. The framework pre-processes user code to identify translation opportunities and prepares optimized translation paths before execution, thereby reducing runtime translation overhead while maintaining code portability across different cloud platforms.
Data Source
AI summary
Methods, storage media, and systems for translating a software expression from a user application programming interface (API) call to an API call of a software development kit (SDK) are disclosed. Some examples may include: receiving a tagged expression indicating that a translation of the software expression from a user API call to an API call of an SDK is to be performed, the SDK being associated with a cloud-native high-performance computing environment, processing an abstract syntax tree associated with the software expression, the processing including replacing symbols in the abstract syntax tree with respective variables, replacing a return statement in the abstract syntax tree with a serialization instruction to write a result to local storage, and serializing the processed abstract syntax tree and providing the serialized abstract syntax tree and one or more resource files to the cloud-native high-performance computing environment for execution.