Unified Compiler IR for Heterogeneous Compute-Communication Scheduling
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Solution Overview
Problem
Existing systems face challenges in efficiently managing communication and computation for complex workloads across heterogeneous processing devices, leading to suboptimal performance and increased development costs due to the complexity of parallelization strategies and communication overhead.
Innovation Solution
A compiler generates a unified intermediate representation (Comm-IR) that integrates computation and communication, using a compute analyzer and communication scheduler to analyze source programs, identify dependencies, and optimize workload distribution across processing devices, thereby reducing inter-node communication and enhancing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If computations are distributed across heterogeneous processing devices, then computational power and processing capacity are improved, but communication overhead and coordination complexity increase
Solution Approach 1:
The patent combines computation and communication operations into a unified intermediate representation (IR) that captures both computational tasks and communication dependencies. This merging allows the compiler to jointly optimize computation distribution and communication patterns, reducing redundant data transfers and synchronization operations across heterogeneous processing devices.
Solution Approach 2:
The patent segments the unified IR into computation-specific and communication-specific components that can be independently analyzed and optimized. By separating these concerns while maintaining their relationships, the system can apply specialized optimization techniques for each type of operation while preserving the overall coordination efficiency across distributed processing devices.
2Productivity
If parallelization strategies are implemented across multiple processing devices, then processing throughput is improved, but system complexity and development cost increase
Solution Approach 1:
The patent implements self-service through automated compiler-based optimization that automatically analyzes the unified IR, identifies parallelization opportunities, and generates optimized execution plans without requiring manual intervention. The system autonomously handles the complexity of distributing computations and coordinating communications across heterogeneous devices, shielding developers from system complexity while maximizing throughput.
Solution Approach 2:
The patent changes the representation parameters by introducing a unified IR that explicitly models both computation and communication with associated metadata about data dependencies, timing constraints, and device capabilities. This parameter transformation enables automated analysis and optimization algorithms to systematically explore parallelization strategies and generate efficient execution plans.
3Speed
If communication operations are explicitly managed between processing devices, then data transfer efficiency is improved, but programming complexity and overhead increase
Solution Approach 1:
The patent introduces a unified intermediate representation as an intermediary layer between high-level program specifications and low-level execution details. This unified IR captures communication operations along with computation operations in a unified framework, allowing the compiler to automatically optimize data transfer patterns without requiring programmers to explicitly manage communication complexity.
Data Source
AI summary
Systems and methods for computation and communication using processing devices are disclosed. The system includes a first processing device and a computing device including a processor and a memory. The memory stores instructions that, when executed by the processor, cause the processor to: identify a source program; identify a first computation identified in the source program; identify a first communication operation associated with the first computation; generate a data structure based on the first computation and the first communication operation; generate a machine-readable code based on the data structure; and transmit the machine-readable code for performing at least one of the first computation or the first communication operation by the first processing device.