Comment intégrer du code accéléré par GPU à d’autres langages et outils dans votre flux de travail ?

The choice of the GPU framework can depend on several factors such as 1. Underlying hardware Some APIs such as Cuda are designed for Nvidia specific hardware which support cuda cores, whereas others such as OpenACC or OpenCL can work across various types of hardware. Selecting a hardware specific framework can give you better options for optimizations at the cost of interoperability. 2. Performance vs Interoperability This tradeoff can have a significant impact on the choice of framework. Some frameworks are more verbose but require more hardware specific knowledge and considerations, while others are portable but lack the fine grain control over features such as types of memory transfers, GPU specific optimizations, etc.

UTM Parameters: When including links in your Snapchat stories, add UTM parameters to the URLs. UTM parameters are tags added to the end of a URL, allowing you to track the source, medium, campaign, and other details of the traffic generated by your Snapchat stories. For example, you can use parameters like utm_source=Snapchat and utm_medium=story to identify traffic coming from your stories.

Using interoperability features is useful for data science applications where a large mix of softwares are used. Using open source frameworks such as Apache Arrow for unifying memory architechtures across applications can help developers in keeping data formats consistent across different softwares in the application pipeline. If you're using Nvidia based GPUs, using libraries such as RAPIDS can help you keep your codebase interoperable across different applications while also taking advantage of hardware specific optimizations. RAPIDS is based on CUDA and Apache Arrow and provides APIs similar to popular frameworks in the data science ecosystem with accelarated performance due to the underlying optimizations.

Custom Landing Pages: Create custom landing pages specifically for your Snapchat stories. By directing users to unique landing pages, you can track visits and actions originating from your Snapchat content more accurately.

I find that the most crucial part of most projects is profiling and benchmarking. Finding the right data-structures to optimize memory access can only follow rigorous measurement of the bottlenecks.

The most critical factor in optimizing data transfers between devices is designing the flow of data within your application. For this, it is very important to identify the requirement of data locality in your application. Some data may only need to be accessed by the CPU or the GPU whereas some data may need to be accessed by both. Analyzing these factors based on usage frequency and designing the data flow to minimize devices access is essential to optimize data transfers. Another factor in optimizing data transfers is the size of data. Data transfers should only contain data that is to be used by the GPU and any extra metadata should not be transfered. This can be achieved by using appropriate data structures for reducing memory size.

Promo Codes: If your promotional content includes discount codes or promo offers, use unique codes for your Snapchat audience. By tracking the usage of these codes, you can directly attribute conversions to your Snapchat stories.

GPU accelerated task having own challenges. Main challenges like in case of Task execution failure, you will get only Error code at most of the case. i.e. Task execution failure, kernal failure, Cuda related Api calls. Library like Cuda having open discussion platform like Nvidia Developer community, but if you face something unusual Errors then it's very challenging to get exact rootcause. Tools like Cuda-Gdb, memcheck, Nsight can work with your visual studio local application but most challenging part is to debug your application when it's deployed on specific environment or Hardware.

Using profiling tools for monitoring GPU code is all about understanding the metrics which can effect the performance of your code. One must focus on maximizing branch efficiency, minimizing memory bandwidth usage and thread launch failures among others to ensure the efficiency and correctness of a GPU based task. Understanding what metrics are important for your application and what tradeoffs can be made in metric performances is important in optimizing the overall service quality Nvidia has an excellent ecosystem for GPU monitoring which comprises of tools like nv-prof, Nsight, cuda-gdb among others.

Attribution Tools: Utilize third-party attribution tools that integrate with Snapchat to track and attribute conversions. These tools can provide detailed insights into user actions after engaging with your stories.

Snap Pixel: Consider using Snap Pixel, which is a tracking tool provided by Snapchat for measuring conversions and optimizing ad performance. Snap Pixel can help you track specific actions taken on your website after users interact with your Snapchat content.