At NIPS 2017, NVIDIA announced new software releases for deep learning and HPC developers. The latest SDK updates include new capabilities and performance…
Overview
NVIDIA has announced updates to its SDK, including new releases of TensorRT, CUDA, and the CUTLASS library, aimed at enhancing performance for deep learning and HPC developers. These updates provide significant improvements in inference speed, productivity, and new functionalities for developers.
What You'll Learn
1
How to optimize TensorFlow models using TensorRT 3
2
Why using CUTLASS can enhance GEMM operations in CUDA applications
3
How to leverage new features in CUDA 9.1 for AI and HPC applications
Prerequisites & Requirements
- Understanding of deep learning frameworks like TensorFlow
- Familiarity with CUDA and NVIDIA GPUs
Key Questions Answered
What performance improvements does TensorRT 3 offer?
TensorRT 3 delivers up to 3.7x faster inference on Tesla V100 compared to Tesla P100, achieving real-time latency under 7ms. It also optimizes TensorFlow models up to 18x faster than the TensorFlow framework inference on Tesla V100.
How can developers use CUTLASS in their applications?
CUTLASS is a CUDA C++ template library that provides a high-level interface for implementing efficient GEMM operations. It is currently open source and under development, allowing developers to contribute feedback and test its capabilities.
What new features are included in CUDA 9.1?
CUDA 9.1 introduces new algorithms for image augmentation, APIs for running batched neural machine translations on Volta Tensor cores, and optimizations that allow launching CUDA kernels up to 12x faster. These enhancements are designed to speed up AI and HPC applications on Volta GPUs.
Key Statistics & Figures
Inference speed improvement
3.7x faster
Compared to Tesla P100 under 7ms real-time latency
Model optimization speed
18x faster
Compared to TensorFlow framework inference on Tesla V100
Kernel launch speed
up to 12x faster
Due to new performance optimizations in CUDA 9.1
Technologies & Tools
Deep Learning Framework
Tensorrt
Used for optimizing and deploying deep learning models
Parallel Computing Platform
Cuda
Provides the foundation for GPU programming and performance optimizations
Cuda Library
Cutlass
Offers templates for efficient GEMM operations
Embedded Software
Jetpack
Supports TensorFlow models and improves performance for deep learning applications
Key Actionable Insights
1Utilizing TensorRT 3 can drastically improve the performance of deep learning applications, especially those running on Tesla V100 GPUs.By optimizing TensorFlow models with TensorRT, developers can achieve significant speed improvements, which is crucial for real-time applications in AI.
2Incorporating CUTLASS into your CUDA projects can streamline the development of linear algebra operations.This library provides essential building blocks for GEMM operations, making it easier for developers to implement high-performance computing solutions.
3Adopting the new features in CUDA 9.1 can enhance the efficiency of AI and HPC applications.The new APIs and optimizations allow developers to leverage advanced GPU capabilities, making it essential for those working with large datasets and complex models.
Common Pitfalls
1
Assuming that all deep learning frameworks will perform equally on different hardware.
Performance can vary significantly based on the optimizations available for specific frameworks and hardware. Developers should benchmark their applications on the target hardware to ensure optimal performance.
Related Concepts
Deep Learning Optimization Techniques
High-performance Computing Strategies
Cuda Programming Best Practices