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Parallel Programming With Cuda Tutorial Part 2 Basics By Saaduddin Mahmud Medium from miro.medium.com

A basic cpu with no ilp will execute the above five steps sequentially. Using cuda, one can utilize the power of nvidia gpus to perform general computing tasks, such as multiplying matrices and performing other linear algebra operations, instead of just doing graphical calculations. Together, the blocks of parallel threads make up what is known as the grid.

To allocate data in unified memory, call cudamallocmanaged(), which returns a pointer that you can access from host (cpu) code or device (gpu) code.

This is not possible in a simple pipelined chip. // cuda device properties variable cudagetdeviceproperties( &prop, device_id); I’ll get into the details of what goes inside the angle brackets soon; To take full advantage of all these threads, i should launch the kernel with multiple thread blocks.

Source: 2.bp.blogspot.com

This means that multiple instructions can be started in the same clock cycle.

Source: www.benfrederickson.com

Is performance better or worse than the k80 results?

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Now i want to get this computation running (in parallel) on the many cores of a gpu.

Source: gpuocelot.gatech.edu

I’ll get into the details of what goes inside the angle brackets soon;

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To do this, all i have to do is add the specifier __global__to the function, which tells the cuda c++ compiler that this is a function that runs on the gpu and can be called from cpu code.

Source: cuda-tutorial.github.io

What ht does is that it takes a few more cpu registers, and executes more instructions on the part of the core that is sitting idle.

Source: www.tutorialspoint.com

Each sm can run multiple concurrent thread blocks.

Source: cuda.ce.rit.edu

I simply divide n by the block size (being careful to round up in case n is not a multiple of blocksize).

Source: docs.nvidia.com

As an example, a tesla p100 gpu based on the pascal gpu architecturehas 56 sms, each capable of supporting up to 2048 active threads.