Hardware Accelerator for Hash Table Lookup Optimization
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Solution Overview
Problem
Hash table lookups in computing systems are resource-intensive and inefficient due to the inability of hardware accelerators to chain multiple operations, leading to sub-optimal performance in hash computations and comparisons.
Innovation Solution
A software-hardware co-optimization mechanism that leverages an integrated hardware accelerator and processor to accelerate hash table lookups by offloading computations and comparisons based on key lengths, using asynchronous programming to overcome latency and optimize resource usage, and employing an 'expected result' feature to minimize unnecessary operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hardware accelerators are used for hash computations and comparisons, then processing speed is improved, but device complexity increases
Solution Approach 1:
The patent segments the hash table lookup process into distinct operations (hash computation, comparison operations) that can be independently executed by the hardware accelerator. This allows the accelerator to specialize in specific tasks while maintaining overall system manageability and reducing the complexity burden.
Solution Approach 2:
The patent introduces an intermediary layer (software-hardware co-optimization mechanism) that manages the interaction between the processor and hardware accelerator. This intermediary handles task distribution, result aggregation, and coordination, thereby shielding the complexity of hardware acceleration from the main processing logic.
2Productivity
If hardware accelerators are used for hash computations and comparisons, then productivity is improved, but resource consumption increases
Solution Approach 1:
The patent applies local quality by selectively using the hardware accelerator only for specific operations (hash computations and comparisons) rather than all processing tasks. This targeted approach maximizes productivity improvement while minimizing additional resource consumption, as the accelerator is engaged only when its specialized capabilities provide genuine benefit.
Solution Approach 2:
The patent implements partial action by offloading only the computationally intensive hash computation and comparison operations to the hardware accelerator, while leaving other operations to the general-purpose processor. This partial offloading strategy achieves productivity improvement without the full resource overhead of complete hardware acceleration.
3Productivity
If asynchronous programming is used to overcome latency, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements asynchronous programming to ensure continuity of useful action by allowing the processor to initiate hash computations on the accelerator and immediately proceed with other tasks, rather than waiting idle for completion. This eliminates processor idle time and maintains continuous productive work, improving throughput while the complexity is managed through standard asynchronous programming patterns.
Data Source
AI summary
A processor may determine, based on a length of an input key, whether to compute a hash value based on the input key or cause an accelerator device coupled to the processor to compute the hash value based on the input key. The processor may cause a hash table lookup operation to be performed based on the hash value.