The new Tesla P4 and P40 accelerators are designed to meet the challenges of the modern data center, including efficient deep learning inference.
Overview
The article discusses the introduction of NVIDIA's new Pascal GPUs, specifically the Tesla P4 and P40, which are designed to enhance deep learning inference in data centers. It highlights the growing computational demands of AI applications and how these GPUs meet the needs for efficiency and performance in processing large datasets.
What You'll Learn
1
How to leverage NVIDIA Tesla P4 and P40 for deep learning inference
2
Why using INT8 computations can improve deep learning inference efficiency
3
When to choose Tesla P4 over Tesla P40 based on application needs
Prerequisites & Requirements
- Understanding of deep learning concepts and GPU architectures
Key Questions Answered
What are the key features of NVIDIA Tesla P4 and P40 GPUs?
The NVIDIA Tesla P4 is designed for maximum efficiency with a peak performance of 21.8 INT8 TOP/s, while the Tesla P40 offers 47.0 INT8 TOP/s and is optimized for high throughput in scale-up servers. Both GPUs utilize the Pascal architecture to enhance deep learning inference capabilities.
How do Tesla P4 and P40 compare to previous generation GPUs?
The Tesla P4 is 40x more efficient than the Intel Xeon E5 CPU and 8x more efficient than the Arria 10-115 FPGA in terms of deep learning inference. The Tesla P40 provides up to 4x speedup in inference performance compared to the previous generation M40.
What is NVIDIA TensorRT and how does it enhance inference?
NVIDIA TensorRT is a high-performance inference engine that optimizes trained neural networks for runtime performance. It applies graph optimizations and is designed to deliver maximum throughput for applications like image classification and object detection.
Key Statistics & Figures
Tesla P4 INT8 TOP/s
21.8
This performance metric highlights the efficiency of the Tesla P4 in deep learning inference tasks.
Tesla P40 INT8 TOP/s
47.0
The peak throughput of the Tesla P40 showcases its capability for high-performance applications.
Efficiency comparison of Tesla P4
40x more efficient than Intel Xeon E5 CPU
This emphasizes the significant performance gains achievable with Tesla P4 for deep learning tasks.
Speedup of Tesla P40 over M40
up to 4x
This illustrates the advancements in performance within a short timeframe due to the Pascal architecture improvements.
Technologies & Tools
Inference Engine
Nvidia Tensorrt
Used to optimize deep learning models for maximum inference throughput and efficiency.
GPU
Nvidia Tesla P4
Designed for efficient deep learning inference in data centers.
GPU
Nvidia Tesla P40
Engineered for high throughput in scale-up server environments.
Key Actionable Insights
1Utilizing the new IDP2A and IDP4A instructions in Tesla P4 can significantly enhance inference performance.These instructions enable rapid computation on packed low-precision vectors, which is crucial for applications requiring real-time responses.
2Consider the Tesla P40 for applications that demand high throughput and low latency.With its 3840 CUDA cores and peak FP32 throughput of 12 TeraFLOP/s, the P40 is ideal for scale-up servers where performance is critical.
3Implement TensorRT to optimize your deep learning models for inference.By applying optimizations such as layer fusion, TensorRT can significantly reduce execution time and improve efficiency in deployment.
Common Pitfalls
1
Overlooking the importance of optimizing neural networks for specific GPU architectures.
Failing to tailor models for the hardware can lead to suboptimal performance and increased latency in inference tasks.
Related Concepts
Deep Learning Architectures
GPU Performance Optimization
Inference Engine Design