#Softraid overhead cpu driver

You can see if you are hitting mutex contention within the driver by collecting OS Runtime data, which shows any pthread_mutex_lock calls lasting above a user-settable threshold. This includes any mutex-lock contention that occurs in the driver if doing multi-threaded launching.

#Softraid overhead cpu full

In the Nsight Systems GUI, you would see this as the full duration of the kernel launch API call (the blue ranges on the CUDA API row in Figure 1). This is the overhead of the wrappers around a CUDA kernel on the host CPU side. Here are definitions for a couple of kinds of overhead: CPU wrapper, memory, and GPU launch overhead. If the launch API call takes 10 Р’Вµs on your system, you can only launch at most 100,000 kernels per second. For example, consider the overhead of the CPU code for launching a CUDA kernel. It is time spent (latency) with no useful kernel work done. We define overhead as the time it takes to perform some operation that youРІР‚в„ўd ideally want to take zero time, and this ends up limiting the rate at which you can do that operation.

Customer screenshot showing latency to start a CUDA kernel. The point of using asynchronous launches from the CPU is to allow the CPU to send commands to the GPU and then perform other tasks while the GPU executes the commands.Р”Р‡igure 1. The long gap between the 10th API call and the 10th launch would be a high latency, but an intentional one. But the 10th kernel in the sequence might have a large launch latency by this definition, because the CPU enqueued the 10 launch commands and returned quickly, while the GPU has to complete the first nine kernels before it can start the 10th. YouРІР‚в„ўd expect minimal latency from the first API call to the first kernel execution. Imagine that you make 10 short API calls to launch a dependent sequence of 10 large kernels. Latency is not always bad in asynchronous systems.

In this post, we mostly talk about launch latency. Task latency, or total time, is the time between adding a task to the queue and the task finishing. This definition includes the time of the launch API call. There are about 20 Р’Вµs of launch latency in Figure 1 between the beginning of the launch call (in the CUDA API row) and the beginning of the kernel execution (in the CUDA Tesla V100-SXM row). For example, CUDA kernel launch latency could be defined as the time range from the beginning of the launch API call to the beginning of the kernel execution. Launch latency, sometimes called induction time, is the time between requesting an asynchronous task and beginning to execute it. There are two common definitions of latency. First, here are some definitions of latency and overhead regarding CUDA.