Multi-Value Memory Access Ordering for Code-Dense Processors
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Solution Overview
Problem
Existing data processing systems face challenges in improving code density while maintaining observability requirements for stricter consistency models, particularly when using single access ordering constrained access instructions like load acquire and store release instructions.
Innovation Solution
A new ordering constrained access instruction is introduced that allows accessing multiple data values, accompanied by an ordering indication to ensure that the accesses are externally observable in the required order, meeting the same observability requirements as multiple single access instructions would.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transaction processing systems use traditional sequential processing methods, then data consistency and reliability are maintained, but processing speed and system performance deteriorate
Solution Approach 1:
The patent segments the transaction processing system into multiple independent processing threads that can execute concurrently. Each thread handles specific transaction operations independently, allowing parallel processing while maintaining data consistency through controlled access to shared resources. This segmentation enables the system to process multiple transactions simultaneously, dramatically improving throughput without sacrificing reliability.
Solution Approach 2:
The patent introduces intermediary mechanisms such as mutex locks and condition variables that mediate access to shared resources between concurrent threads. These intermediaries coordinate thread execution, ensuring that critical sections are accessed exclusively and that data consistency is maintained even during parallel processing. The intermediary acts as a controller that enables concurrent execution while preventing race conditions.
2Reliability
If the system implements strict ordering constraints for access operations, then data integrity is ensured, but processing efficiency and throughput deteriorate
Solution Approach 1:
The patent implements dynamic ordering constraints that adapt to the specific characteristics of each transaction and data access pattern. Rather than enforcing rigid global ordering, the system dynamically determines ordering requirements based on transaction types, data dependencies, and current system state. This dynamic approach allows the system to relax ordering constraints where unnecessary, thereby improving throughput while maintaining data integrity where required.
Solution Approach 2:
The patent changes the parameter of ordering constraints from fixed to variable, allowing the system to adjust the strictness of ordering requirements based on transaction priorities, data criticality, and system load. By parameterizing the ordering constraints, the system can optimize the balance between data integrity and processing efficiency for different transaction scenarios, enabling higher throughput for non-critical operations while maintaining strict ordering for critical data access.
3Productivity
If the system allows concurrent access operations without ordering constraints, then processing speed improves, but data consistency and reliability deteriorate
Solution Approach 1:
The patent applies preliminary actions by establishing ordering constraints and synchronization protocols before concurrent access operations begin. Mutex locks are acquired in advance before entering critical sections, and transaction schedules are predetermined based on dependency analysis. This preliminary preparation ensures that when concurrent operations execute, data consistency is already guaranteed by the pre-established ordering framework, allowing high-speed parallel processing without compromising reliability.
4Reliability
If the system uses complex synchronization mechanisms to maintain ordering, then data consistency is preserved, but system complexity and overhead increase
Solution Approach 1:
The patent applies local quality by implementing fine-grained synchronization where ordering constraints and synchronization mechanisms are applied only to specific critical sections and shared resources that require them, rather than globally across the entire system. Each thread and critical section has localized synchronization logic tailored to its specific data access patterns. This localized approach maintains data consistency for critical resources while minimizing the complexity overhead for non-critical operations, allowing concurrent access without universal strict ordering.
Data Source
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AI summary
Processing circuitry is provided to perform operations, along with instruction decoder circuitry to decode instructions to control the processing circuitry to perform the operations specified by the instructions. A set of registers is used to hold data values for access by the processing circuitry. The instruction decoder circuitry is responsive to an ordering constrained access instruction used to access multiple data values, and providing register indication information and memory address information, to control the processing circuitry to perform a sequence of access operations, where each access operation causes a data value from amongst the multiple data values to be moved between an associated register determined from the register indication information and an associated memory address determined from the memory address information. Further, an ordering indication is derived from the ordering constrained access instruction and used to determine an order in which the multiple data values are to be accessed when performing the sequence of access operations, to thereby ensure that observability conditions required when implementing the ordering constrained access instruction are met.