Unified Compiler IR for Heterogeneous Compute-Communication Partitioning
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Solution Overview
Problem
Existing systems face challenges in efficiently managing parallel computations and communication operations, especially for complex workloads and heterogeneous processing devices, leading to suboptimal performance and increased communication overhead.
Innovation Solution
A compiler-based approach that generates a unified intermediate representation (Comm-IR) combining computation and communication, using a compute analyzer and communication scheduler to optimize workload partitioning and communication strategies based on device configurations, reducing inter-node communication and synchronizing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If parallel computations are distributed across multiple processing devices, then computational capacity increases, but communication overhead increases
Solution Approach 1:
The system segments computations into compute operations and communication operations, allowing independent optimization and scheduling of each type. The compiler analyzes the source program to identify and separate computation kernels from communication operations, enabling efficient parallel execution while minimizing communication overhead.
Solution Approach 2:
The compiler performs preliminary analysis and optimization of communication operations before code generation. It identifies communication patterns, optimizes data transfer strategies, and generates optimized communication code in advance, reducing runtime communication overhead across distributed processing devices.
2Productivity
If computations are partitioned across heterogeneous processing devices, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The system employs a universal compiler framework that handles multiple processing device types through a unified intermediate representation (IR). The compiler generates device-agnostic IR code that can be executed on heterogeneous devices, abstracting away device-specific complexities while maintaining optimization capabilities for each device type.
Solution Approach 2:
The intermediate representation serves as an intermediary layer between the source program and device-specific execution code. This IR layer captures computation and communication operations in a device-independent format, allowing the compiler to optimize for heterogeneous devices without exposing complexity to the programmer or requiring device-specific source code.
3Productivity
If communication operations are integrated with computation operations, then operational efficiency improves, but optimization difficulty increases
Solution Approach 1:
The system merges computation operations and communication operations into a unified intermediate representation. This combined IR allows the compiler to analyze and optimize the interaction between computation and communication, identifying opportunities for overlap, pipelining, and synchronization optimization that would be difficult to achieve with separate handling.
Solution Approach 2:
The compiler transforms the unified IR into device-specific code by adjusting parameters and optimizations based on the target processing device characteristics. It modifies communication and computation parameters to match device capabilities, enabling efficient execution on heterogeneous architectures while maintaining the benefits of integrated operation analysis.
Data Source
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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.