Compiler Global-Id Address Generation for GPGPU Memory Optimization
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Solution Overview
Problem
In GPGPU models, existing methods for generating machine code for driving execution units result in excessive calculations for memory addresses, leading to inefficient performance in load, store, and sample operations.
Innovation Solution
A method involving a compiler that collects data access instructions associated with the same memory surface, analyzes address patterns to generate global-id addresses, and generates machine code containing these addresses to optimize memory access operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional address calculation methods are used in GPGPU, then memory access operations can be performed, but the computational cost of memory addresses becomes excessive
Solution Approach 1:
The patent applies preliminary action by pre-calculating and organizing address patterns during the compilation phase. The compiler analyzes data access instructions and generates optimized address calculation code before execution, so that during runtime the address calculation requires fewer computational steps. This shifts the computational workload from runtime to compile time, improving runtime performance while reducing operational energy consumption.
Solution Approach 2:
The patent changes parameters by transforming the address calculation approach from generic to specialized. It identifies specific address patterns (such as coalesced access patterns) and generates optimized machine code with tailored address calculation instructions for each pattern. This parameter transformation allows the system to use more efficient calculation methods matched to specific access patterns, reducing computational cost while maintaining productivity.
2Productivity
If traditional address calculation methods are used, then memory operations can be executed, but the hit ratio of cached data decreases
Solution Approach 1:
The patent applies segmentation by dividing memory access operations into distinct categories based on their address patterns. It segments data access instructions into different groups (such as sequential access, strided access, scattered access) and generates specialized address calculation code for each segment. This segmentation allows the compiler to optimize each category independently, improving cache hit ratios by ensuring that accesses within the same segment follow predictable patterns that better utilize cache structures.
Solution Approach 2:
The patent uses preliminary action by performing address pattern analysis and optimization during the compilation phase rather than at runtime. The compiler examines all data access instructions, identifies patterns, and generates optimized address calculation code in advance. This preliminary analysis enables the system to prepare optimized code paths that improve cache utilization without adding runtime complexity, effectively resolving the contradiction between improved hit ratio and reduced complexity.
3Productivity
If optimized machine code is generated through address pattern analysis, then load, store, and sample performance improves, but the compiler complexity increases
Solution Approach 1:
The patent applies copying by creating specialized copies of address calculation code for different access patterns. Instead of using a single generic address calculation routine, the compiler generates multiple specialized code versions (copies) tailored to specific patterns such as coalesced access, strided access, and random access. Each copy is optimized for its specific pattern, improving load, store, and sample performance. The copying approach allows the compiler to manage complexity by organizing optimizations into reusable pattern-specific templates rather than implementing complex conditional logic for each case.
Data Source
AI summary
A method for generating machine code for driving an execution unit is introduced to incorporate with at least the following steps: Data access instructions of a kernel, which are associated with the same memory surface, are collected. An address pattern associated with the data access instructions is analyzed to generate a global-id address. Machine code containing the global-id address is generated.