Google llc (20240211413). Streaming Transfers and Ordering Model simplified abstract
Contents
Streaming Transfers and Ordering Model
Organization Name
Inventor(s)
Rahul Nagarajan of San Jose CA (US)
Arpith Chacko Jacob of Los Altos CA (US)
Suvinay Subramanian of Sunnyvale CA (US)
Hema Hariharan of Cupertino CA (US)
Streaming Transfers and Ordering Model - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211413 titled 'Streaming Transfers and Ordering Model
The hardware/software interface described in this patent application enables asynchronous data movement between off-core memory and core-local memory, known as "stream transfers," along with a stream ordering model. This innovation allows for more efficient expression of common data movement patterns, particularly in sparse workloads. Direct stream instructions within a stream are processed in order, while indirect stream instructions process offset elements in an offset list in order. A sync flag is updated to indicate monotonic incremental progress for the stream.
- Hardware/software interface for asynchronous data movement
- Stream transfers between off-core memory and core-local memory
- Stream ordering model for efficient data movement patterns
- Direct and indirect stream instructions processed in order
- Sync flag updated to indicate progress for the stream
Potential Applications: - High-performance computing - Data analytics - Artificial intelligence and machine learning - Cloud computing - Internet of Things (IoT) devices
Problems Solved: - Efficient data movement in sparse workloads - Streamlining common data movement patterns - Monotonic incremental progress tracking - Improved performance in memory-intensive applications - Enhanced synchronization between different memory types
Benefits: - Increased efficiency in data movement - Improved performance in memory-bound applications - Simplified expression of data movement patterns - Better synchronization between off-core and core-local memory - Enhanced scalability for large-scale computing tasks
Commercial Applications: Title: "Enhanced Data Movement Interface for High-Performance Computing" This technology could be utilized in supercomputing centers, data centers, research institutions, and cloud computing providers to optimize data movement and improve overall system performance. The market implications include faster processing speeds, reduced latency, and enhanced scalability for memory-intensive applications.
Questions about the technology: 1. How does this hardware/software interface improve data movement efficiency in sparse workloads? 2. What are the key advantages of using stream transfers between off-core memory and core-local memory for high-performance computing tasks?
Frequently Updated Research: Researchers in the field of computer architecture and high-performance computing are continually exploring new ways to optimize data movement between different types of memory to enhance overall system performance. Stay updated on the latest advancements in stream transfers and stream ordering models for improved efficiency in memory-bound applications.
Original Abstract Submitted
generally disclosed herein is a hardware/software interface for asynchronous data movement between an off-core memory and a core-local memory, referred to as “stream transfers”, and a stream ordering model. the stream transfers allow software to more efficiently express common data-movement patterns, specifically ones seen in sparse workloads. direct stream instructions that belong to a stream are processed in-order. for indirect stream instructions, offset elements in an offset list are processed in order. a sync flag is updated to indicate monotonic incremental progress for the stream.
