Which techniques would not be used to optimize parallel CUDA code?

Table 2

Does CUDA increase performance?

Upgrading Your Graphics Card Using a graphics card that comes equipped with CUDA cores will give your PC an edge in overall performance, as well as in gaming. More CUDA cores mean clearer and more lifelike graphics.

How do I optimize my GPU code?

1. Find ways to parallelize sequential code.
2. Adjust kernel launch configuration to maximize device utilization.
3. Ensure global memory accesses are coalesced.
4. Minimize redundant accesses to global memory.
5. Avoid different execution paths within the same warp.
6. Minimize data transfers between the host and the device.

What is the peak performance given by GPUs?

‘Theoretical peak performance’ numbers appear to be determined by adding together the theoretical performances of the processing components of the GPU, which are calculated by multiplying the clock speed of the component by the number of instructions it can perform per cycle.

What is CUDA graph?

CUDA graphs are a model for work submission in CUDA that helps improve this situation. A graph is a series of operations (such as kernel launches) connected by dependencies, which are defined separately from their execution. This allows a graph to be defined once and then launched repeatedly.

How does CUDA memory and cache architecture work?

All modern CUDA capable cards (Fermi architecture and later) have a fully coherent L2 Cache. As with memory, the GPU’s L2 cache is much smaller than a typical CPU’s L2 or L3 cache, but has much higher bandwidth available. Unlike most high end CPUs which have 4 or 6 cores, high performance CUDA GPUs have 16 SMs.

What are CUDA cores good for?

CUDA Cores are parallel processors, just like your CPU might be a dual- or quad-core device, nVidia GPUs host several hundred or thousand cores. The cores are responsible for processing all the data that is fed into and out of the GPU, performing game graphics calculations that are resolved visually to the end-user.

What does Nvidia CUDA do?

CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

What does GPU optimization do?

“GPU driver optimization” just means your driver/GPU software always checks to make sure it has the latest driver for your hardware. It makes it so the graphics of your games are produced at their best, with the least speed reduction on your system.

What is a graphic API?

Graphics API (Application Programming Interface), like every interface, is just the means of communication – standardized, documented definition of functions and other stuff that is used on the application’s side and implemented by the driver. Driver translates these calls to commands specific to particular hardware.

What is theoretical peak performance?

The theoretical peak performance is sometimes referred to as: Speed that the vendor is guaranteed never to exceed. The computational speed of light of the system. The fastest a machine can run without software or data.

What is Cuda and how does it work?

CUDA® is a parallel computing platform and programming model that enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU). Since its introduction in 2006, CUDA has been widely deployed through thousands of applications and published research papers,…

What is the best practices guide for NVIDIA CUDA?

This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA ® CUDA ® GPUs. It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures.

How does the CUDA Runtime API work with the GPU?

This feature of the CUDA Runtime API makes launching kernels on the GPU very natural and easy—it is almost the same as calling a C function. There are only two lines in our saxpy kernel. As mentioned earlier, the kernel is executed by multiple threads in parallel.

Is CUDA C/C++ hardware specific?

No. CUDA C/C++ provides an abstraction; it’s a means for you to express how you want your program to execute. The compiler generates PTX code which is also not hardware specific. At run-time the PTX is compiled for a specific target GPU – this is the responsibility of the driver which is updated every time a new GPU is released.