GPU Parallel Memory Diagnostics for Pre-Boot Boot Time Reduction
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Solution Overview
Problem
Existing memory diagnostics in information handling systems are inefficient due to serial processing in pre-boot environments, lack of GPU utilization, and infrequent execution, leading to prolonged boot times as memory sizes increase.
Innovation Solution
Employing a graphics processing unit (GPU) with multiple processing cores to perform parallel memory failure testing during the pre-boot phase, allowing the system BIOS to select memory regions for testing and offload diagnostics tasks to the GPU for efficient scanning on every boot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-boot memory diagnostics are performed using a single-threaded UEFI BIOS, then memory testing can be completed, but the boot time increases significantly especially as memory sizes increase
Solution Approach 1:
The memory testing process is segmented into multiple independent regions that can be tested simultaneously. The BIOS divides the memory space into multiple regions and assigns each region to a separate processing core on the GPU, enabling parallel execution of diagnostic tests across different memory segments.
Solution Approach 2:
The patent replaces the traditional single-threaded CPU-based diagnostic mechanism with a GPU-based parallel processing system. The GPU's multiple processing cores substitute for the sequential CPU execution model, fundamentally changing the mechanical approach from serial to parallel memory testing.
2Reliability
If memory diagnostics are run at every boot to ensure comprehensive testing, then memory reliability is improved, but the time loss and performance impact increase
Solution Approach 1:
Memory diagnostics are segmented into region-based tasks that can be distributed across multiple GPU cores. This segmentation allows the system to perform comprehensive memory testing by dividing it into manageable parallel units, making frequent execution feasible without excessive time penalty.
Solution Approach 2:
The system implements periodic memory diagnostics where the BIOS can choose to execute memory tests at regular boot intervals or based on configured schedules. The parallel GPU-based approach enables periodic comprehensive testing with reduced time overhead compared to traditional serial methods.
3Device complexity
If traditional CPU-based serial memory testing is used, then implementation is simple, but productivity and scanning efficiency are low
Solution Approach 1:
The patent substitutes the CPU's single-threaded execution mechanism with the GPU's multi-core parallel processing architecture. This mechanical substitution replaces sequential instruction execution with simultaneous parallel thread execution, dramatically improving memory scanning productivity while maintaining manageable implementation complexity through standardized GPU interfaces.
Solution Approach 2:
The GPU processing cores are utilized for memory diagnostics in addition to their primary graphics processing function. This multi-functionality allows the same hardware resource to serve dual purposes: graphics rendering during normal operation and parallel memory testing during boot, thereby improving scanning efficiency without adding dedicated diagnostic hardware.
4Productivity
If GPU parallel processing is used for memory diagnostics, then scanning efficiency and productivity improve, but device complexity increases
Solution Approach 1:
The patent leverages the existing GPU hardware already present in modern systems for its primary graphics function and repurposes it for memory diagnostics. This universality approach increases scanning efficiency without adding new dedicated diagnostic devices, thereby limiting the increase in overall system complexity.
Solution Approach 2:
The BIOS acts as an intermediary layer that manages the interface between the CPU and GPU for memory diagnostic operations. It handles task distribution, coordinate parallel testing across GPU cores, and aggregate results, thereby managing the complexity of GPU integration without requiring changes to the underlying hardware architecture.
Data Source
AI summary
An information handling system may include a central processing unit (CPU), a graphics processing unit (GPU) including a plurality of processing cores, a memory coupled to the CPU and to the GPU, and a basic input/output system (BIOS). While the information handling system is in a pre-boot environment and prior to initialization of an operating system of the information handling system, the BIOS may cause the central processing unit to select respective portions of the memory for failure testing; and cause individual ones of the plurality of processing cores of the GPU to carry out the failure testing of the respective portions of the memory.