Offloading Storage Tasks to GPU via Host Memory Buffer
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Solution Overview
Problem
Data storage devices face challenges in handling computationally intensive tasks such as machine learning and error correction due to limited power and performance capabilities compared to host system CPUs or GPUs, necessitating a reliable method to offload processing tasks to GPU resources.
Innovation Solution
A system that utilizes host memory buffers to coordinate tasks between data storage devices and GPUs, enabling direct memory access for task input and output data and processing offload services to initiate processing tasks on GPUs, with features like error correction and machine learning model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data storage devices use their own CPUs for processing tasks, then they can handle storage operations independently, but the processing power is limited compared to host GPUs
Solution Approach 1:
The host system acts as an intermediary between the storage device and GPU. The storage device offloads computationally intensive tasks to the host GPU through standardized interfaces, allowing access to powerful graphics processing resources without direct physical connection. This resolves the contradiction by enabling high processing power through the host while maintaining storage device independence.
Solution Approach 2:
The storage device CPU is designed to handle multiple functions: traditional storage operations and task offloading coordination. By making the storage controller multi-functional, the system can independently manage storage tasks while also coordinating with host GPUs for intensive processing, thus improving processing capability without requiring dedicated specialized hardware for each function.
2Productivity
If data storage devices incorporate custom hardware accelerators, then they can achieve higher throughput performance, but the computation resources are still limited compared to host GPUs
Solution Approach 1:
The host system serves as an intermediary that bridges the storage device and external GPU resources. Instead of being limited by onboard accelerators, the storage device can offload tasks to the host's powerful GPUs through standardized interfaces, thereby accessing vastly superior computation resources while maintaining its own throughput performance capabilities.
Solution Approach 2:
The solution moves the computation resources from a local dimension (onboard accelerators within the storage device) to a remote dimension (host system GPUs). This dimensional shift allows the storage device to access much larger computation resources without increasing its own physical size or power consumption, effectively resolving the limitation of onboard accelerator power.
3Reliability
If storage device CPUs are designed to meet peak capacity demands, then they can support target storage bandwidth and QoS, but they lack the computational resources for heroic data recovery and machine learning tasks
Solution Approach 1:
The storage device CPU is designed with multi-functionality to handle both traditional storage operations (maintaining reliability and QoS) and task offloading coordination (enabling computational versatility). This allows the same processor to ensure peak performance for storage bandwidth while also managing complex tasks like heroic data recovery and machine learning through GPU offloading.
Solution Approach 2:
The host system acts as an intermediary that provides the additional computational resources needed for versatile tasks. The storage device CPU maintains its optimized design for reliable storage operations, while the host GPU provides the extra computational power for adaptive tasks like machine learning and heroic data recovery, thus preserving reliability while gaining versatility.
4Reliability
If data storage devices handle computationally intensive tasks locally, then they can maintain operational independence, but they consume more power and have limited processing capability
Solution Approach 1:
The host system serves as an intermediary that enables the storage device to access powerful GPU resources without the storage device itself consuming the associated power. The storage device maintains operational independence by managing the offloading process, while the host system provides the computational power, thus separating the power consumption from the storage device.
Solution Approach 2:
The computationally intensive tasks are extracted from the storage device and executed on the host GPU. This extraction removes the power consumption burden from the storage device while maintaining its operational independence in managing and coordinating these tasks. The storage device only consumes power for essential storage operations and task management, not for the intensive processing itself.
Data Source
AI summary
Systems and methods for offloading data storage processing tasks from a data storage device to a graphics processing unit data are described. Data storage devices may include a peripheral interface configured to connect to a host system and provide access to a host memory buffer. The data storage device may store task input data to the host memory buffer. The data storage device may notify a processor device including the graphics processing unit to initiate the storage processing task. The processor device may access the task input data from the host memory buffer and store the task output data to the host memory buffer for access by the data storage device.