Database Accelerator Pipeline for Decompression and Filter Throughput
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computing systems face inefficiencies in processing large datasets due to limitations in hardware acceleration and resource management, particularly in data centers, leading to suboptimal performance and resource utilization.
Innovation Solution
Implementing a data center design with robotically accessible sleds, optical fiber connectivity, and a unified network architecture that supports multiple protocols, enabling resource pooling and dynamic reallocation based on workload prediction, along with hardware accelerators like FPGAs for enhanced computing performance and thermal management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional hardware architectures are used for data processing, then system complexity is manageable, but computing performance and resource utilization are insufficient
Solution Approach 1:
The system is divided into multiple independent sleds that can be independently managed, configured, and replaced. Each sled contains specific functional components (processors, memory, accelerators, storage) that can be optimized separately, allowing high computing performance without overwhelming system complexity.
Solution Approach 2:
The sleds are designed with universal interfaces and standardized configurations that allow them to perform multiple functions. The same sled architecture can support different workloads and be dynamically reconfigured, enabling a single hardware platform to handle diverse computing tasks efficiently.
2Speed
If components are operated at higher frequencies and power levels to improve performance, then computing speed increases, but thermal management becomes more difficult
Solution Approach 1:
By segmenting the system into multiple sleds with distributed components, heat generation is spread across multiple physical locations rather than concentrated in a single processor socket. This distributed thermal profile enables higher overall system power levels while maintaining manageable local temperatures.
Solution Approach 2:
The patent transitions from a two-dimensional processor layout to a three-dimensional architecture where components are stacked vertically across multiple sleds and layers. This vertical dimension provides additional thermal pathways and improves airflow for heat dissipation, enabling higher operating frequencies without thermal bottlenecks.
3Productivity
If resource pooling and dynamic reallocation are implemented to improve resource utilization, then system efficiency increases, but control and management complexity increases
Solution Approach 1:
The system implements dynamic resource allocation where sleds and components can be hot-swapped, reconfigured, and reallocated based on workload demands. The management system dynamically tracks resource usage and automatically redistributes workloads across available capacity, optimizing utilization without requiring manual intervention.
Solution Approach 2:
The resource management system continuously monitors system state, workload demands, and resource utilization metrics, then uses this feedback to make real-time allocation decisions. This closed-loop control enables efficient resource pooling while automating the complexity of dynamic reallocation, presenting a simplified interface to users.
4Speed
If optical fiber connectivity and unified network architecture are deployed to enhance data transmission, then bandwidth and speed improve, but system complexity and installation difficulty increase
Solution Approach 1:
The sleds are equipped with universal optical interfaces that can be configured for different network protocols and transmission rates. This standardization allows high-speed optical connectivity to be implemented systematically across all sleds using the same hardware components and connection procedures, reducing integration complexity despite the advanced technology.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Technologies for database acceleration include a computing device having a database accelerator. The database accelerator performs a decompress operation on one or more compressed elements of a compressed database to generate one or more decompressed elements. After decompression of the compressed elements, the database accelerator prepares the one or more decompressed elements to generate one or more prepared elements to be processed by an accelerated filter. The database accelerator then performs the accelerated filter on the one or more prepared elements to generate one or more output elements. Other embodiments are described and claimed.