1. CUDA for Tegra
2. Overview
3. Memory Management
4. Porting Considerations
5. EGL Interoperability
6. CUDA Upgradable Package for Jetson
7. cuDLA
cuda-for-tegra-appnote
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Contents
1. CUDA for Tegra
2. Overview
3. Memory Management
3.1. I/O Coherency
3.2. Estimating Total Allocatable Device Memory on an Integrated GPU Device
4. Porting Considerations
4.1. Memory Selection
4.2. Pinned Memory
4.3. Effective Usage of Unified Memory on Tegra
4.4. GPU Selection
4.5. Synchronization Mechanism Selection
4.6. CUDA Features Not Supported on Tegra
5. EGL Interoperability
5.1. EGLStream
5.1.1. EGLStream Flow
5.1.2. CUDA as Producer
5.1.3. CUDA as Consumer
5.1.4. Implicit Synchronization
5.1.5. Data Transfer Between Producer and Consumer
5.1.6. EGLStream Pipeline
5.2. EGLImage
5.2.1. CUDA interop with EGLImage
5.3. EGLSync
5.3.1. CUDA Interop with EGLSync
5.3.2. Creating EGLSync from a CUDA Event
5.3.3. Creating a CUDA Event from EGLSync
6. CUDA Upgradable Package for Jetson
6.1. Installing the CUDA Upgrade Package
6.1.1. Prerequisite
6.1.2. From Network Repositories or Local Installers
6.2. Deployment Considerations for CUDA Upgrade Package
6.2.1. Use the Right Upgrade Package
6.2.2. Feature Exceptions
6.2.3. Check for Compatibility Support
7. cuDLA
7.1. Developer Guide
7.1.1. Device Model
7.1.2. Loading and Querying Modules
7.1.3. Memory Model
7.1.4. Task Execution and Synchronization Model
7.1.5. Error Reporting Model
7.2. Migrating from NvMediaDla to cuDLA
7.3. Profiling a cuDLA App
7.4. cuDLA Release Notes