Vector Reduction Instructions for Lower ISA Overhead
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Solution Overview
Problem
Existing instruction set architectures (ISAs) lack efficient mechanisms for performing reduction operations on multiple data element values, particularly in vector operations, leading to suboptimal performance in processing tasks that require such operations.
Innovation Solution
The introduction of a vector friendly instruction format that supports various fields and templates for specifying operations on vector operands, including memory access and non-memory access operations, allowing for efficient execution of reduction operations on multiple data elements using a single instruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing instruction set architectures are used for reduction operations, then the processor can execute basic operations, but the performance is suboptimal due to lack of efficient reduction mechanisms
Solution Approach 1:
The patent combines multiple operations (memory access, reduction operation, and write-back) into a single integrated instruction. The execution unit is designed to perform both the reduction operation and memory access atomically in one operation, eliminating the need for separate instructions and intermediate storage operations.
Solution Approach 2:
The reduction operation execution unit is designed with multi-functionality to handle various reduction operations (add, multiply, minimum, maximum, etc.) on different data types (integers, floating-point numbers, vectors) through a unified instruction format with different templates, reducing the need for multiple specialized execution units.
2Productivity
If multiple instructions are used for reduction operations on multiple data elements, then comprehensive processing is achieved, but the number of instructions increases leading to higher overhead
Solution Approach 1:
The patent merges multiple sequential operations into a single atomic operation. The execution unit performs memory access, reduction computation, and result write-back in one unified operation, eliminating the time overhead associated with multiple separate instructions and intermediate storage operations.
Solution Approach 2:
The execution unit is pre-configured with reduction logic and operand selection mechanisms that enable it to perform multiple reduction operations in parallel or sequence without requiring intermediate instruction overhead. The design anticipates multiple operations and prepares the execution pipeline accordingly.
3Productivity
If vector operations are implemented without dedicated reduction mechanisms, then the instruction set remains simple, but performance in processing tasks requiring reduction operations deteriorates
Solution Approach 1:
The execution unit implements a universal reduction operation mechanism that can handle various reduction types (add, multiply, min, max, etc.) and data types through a unified architecture. This multi-functional design achieves high vector processing performance without requiring separate dedicated units for each operation type.
Solution Approach 2:
The patent extends scalar reduction operations to vector operations by adding dimensionality to the execution unit. The same reduction logic is applied across multiple data elements in parallel, achieving vector processing performance through dimensional extension rather than creating entirely separate vector-specific reduction mechanisms.
Data Source
AI summary
Embodiments detailed herein relate to reduction operations on a plurality of data element values. In one embodiment, a process comprises decoding circuitry to decode an instruction and execution circuitry to execute the decoded instruction. The instruction specifies a first input register containing a plurality of data element values, a first index register containing a plurality of indices, and an output register, where each index of the plurality of indices maps to one unique data element position of the first input register. The execution includes to identify data element values that are associated with one another based on the indices, perform one or more reduction operations on the associated data element values based on the identification, and store results of the one or more reduction operations in the output register.