Dynamic Accelerator Chaining for Data Processing Bottlenecks
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Solution Overview
Problem
Current data processing systems face inefficiencies in utilizing accelerators for specialized tasks, leading to suboptimal performance and energy consumption, particularly in tasks like machine learning and graphics processing, where dedicated accelerators like GPUs and FPGAs are not fully leveraged for chained operations.
Innovation Solution
The proposed solution involves chaining multiple accelerators together to perform specialized operations, allowing for optimized resource allocation and improved performance in tasks such as machine learning and graphics processing by pooling resources like GPUs, FPGAs, and neural network accelerators, and providing them to processors on demand, enabling efficient data processing and computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If accelerators are used for specialized tasks, then processing performance is improved, but resource utilization is insufficient
Solution Approach 1:
The system enables accelerators to perform multiple functions by allowing dynamic chaining where the same accelerator can serve different tasks at different times. The method creates a universal accelerator pool that can be allocated to various processing needs, making each accelerator adaptable to different workloads rather than being dedicated to a single function.
Solution Approach 2:
The system dynamically configures chains of accelerators based on workload requirements. Accelerators can be added or removed from chains at runtime, and the configuration can be adjusted to match changing processing demands. This dynamic reconfiguration allows the system to optimize both performance and resource utilization adaptively.
2Productivity
If multiple accelerators are chained together, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces a chain management mechanism that acts as an intermediary between the CPU and multiple accelerators. This mediator handles the complexity of chain configuration, accelerator allocation, and data flow management, shielding users from the underlying system complexity while enabling efficient multi-accelerator processing.
Solution Approach 2:
The system creates virtual representations of accelerator chains that can be configured and managed separately from the physical hardware. By working with virtual chain models, the system simplifies the management of complex accelerator configurations while maintaining the ability to optimize actual hardware utilization.
3Reliability
If accelerators are dedicated to specific tasks, then task performance is improved, but adaptability to different workloads decreases
Solution Approach 1:
The system creates a pool of accelerators that can be dynamically allocated to different workloads. Instead of dedicating accelerators to specific tasks, the same accelerators can serve multiple types of workloads by being added to different chains or reconfigured for different operations, maintaining high performance across diverse tasks.
Solution Approach 2:
The system pre-configures templates for accelerator chains that can be quickly instantiated for different workloads. By preparing chain configurations in advance, the system can rapidly adapt to new tasks without requiring complex real-time reconfiguration, thus maintaining both performance and flexibility.
Data Source
AI summary
A method of an aspect includes receiving a request for a chained accelerator operation, and configuring a chain of accelerators to perform the chained accelerator operation. This may include configuring a first accelerator to access an input data from a source memory location in system memory, process the input data, and generate first intermediate data. This may also include configuring a second accelerator to receive the first intermediate data, without the first intermediate data having been sent to the system memory, process the first intermediate data, and generate additional data. Other apparatus, methods, systems, and machine-readable medium are disclosed.