> Most of the C abstract machine (what I think you meant when you wrote virtual machine) carries over directly.
Tomato tomato, yes I meant the abstract machine.
C's abstractions, however, do not carry over directly. C's programming model is strictly serial, whereas CUDA's model is task parallel.
CUDA assumes a memory hierarchy and separate memory spaces between a host and a device - both concepts are fundamentally unknown in the C programming model.
The lowest level of abstraction in CUDA is a thread, whereas threads are optional in C and follow rules that don't apply in CUDA (and vice versa). There's no thread hierarchy in C, type qualifiers like volatile are specified differently.
The assignment operator in CUDA is a different beast from C's assignment with very specific rules derived from the host/device-separation.
Function parameters behave differently between C and CUDA (e.g. there's no such thing as a 4KiB limit on parameters passed via the __global__ specifier, in fact no such mechanism even exists in C).
I could continue with the different semantics and rules of type qualifiers like "volatile" and "static", scopes, linkage, storage duration, etc. But I won't.
CUDA uses C++ syntax and a few extensions to make C(++) programmers feel at home and provide a heterogeneous computing environment that doesn't rely on API calls (like OpenCL does). That doesn't mean both environments share the same programming model and semantics, starting with the separation of host and device, which isn't a C concept.
Yes, everything you write is true, it's a great list. I wish Nvidia had a section of their programming guide that succinctly stated the differences. I've been writing CUDA for a long time, and once you grok the host/device stuff, it's still mostly just C/C++. I've only been bitten by a few of these things a handful of times over the last 10 years, and only when doing something "fancy".
Tomato tomato, yes I meant the abstract machine.
C's abstractions, however, do not carry over directly. C's programming model is strictly serial, whereas CUDA's model is task parallel.
CUDA assumes a memory hierarchy and separate memory spaces between a host and a device - both concepts are fundamentally unknown in the C programming model.
The lowest level of abstraction in CUDA is a thread, whereas threads are optional in C and follow rules that don't apply in CUDA (and vice versa). There's no thread hierarchy in C, type qualifiers like volatile are specified differently.
The assignment operator in CUDA is a different beast from C's assignment with very specific rules derived from the host/device-separation.
Function parameters behave differently between C and CUDA (e.g. there's no such thing as a 4KiB limit on parameters passed via the __global__ specifier, in fact no such mechanism even exists in C).
I could continue with the different semantics and rules of type qualifiers like "volatile" and "static", scopes, linkage, storage duration, etc. But I won't.
CUDA uses C++ syntax and a few extensions to make C(++) programmers feel at home and provide a heterogeneous computing environment that doesn't rely on API calls (like OpenCL does). That doesn't mean both environments share the same programming model and semantics, starting with the separation of host and device, which isn't a C concept.