If you want to use VirtualAlloc to set aside memory and retrieve it by pages, your first call should only do a MEM_RESERVE on the maximum size of memory you plan to use. Then when you need more, you will make another call using MEM_COMMIT to get access to the page....
To compile C sources to WASM, useEmscripten compiler. The following script compiles librnnoise into a WASM module encapsulated in a JavaScript file: if [[ `uname` == "Darwin" ]]; thenSO_SUFFIX="dylib"elseSO_SUFFIX="so"fiemcc \-Os \-g2 \-s ALLOW_MEMORY_GROWTH=1 \-s MALLOC=emmal...
and if they went to a good university, they have a homework assignment where they are told to uselock cmpxchgto implement a mutex. Once they're all grown up however, (i.e., grad school) we teach them the truth: Locks are
The new() function returns a pointer to the data struct that was created and populated.return p;Here is the new() function in its entirety:void * new (void * _class, ...) { struct Class * class = _class; void * p = malloc(1, class->size); * (struct Class **) p = class...
Copied to Clipboard Error: Could not Copy #define TLISTINSERT(I, V)\ ({\ typeof(I) __tmp, __n, __p;\ __tmp = (typeof(I)) malloc(sizeof(*(I)));\ __n = (I);\ __p = __n->_prev;\ if (__tmp != 0) {\ ...
I implement the loop body with ptx, I think it can avoid the optimization behavior of nvcc. Highly unlikely to be a good idea. The CUDA compiler is based on LLVM, an extremly powerful framework for code transformations, i.e. optimizations. If you run into the compiler optimizing away code...
Go to 1. Every design decision is driven by whatever is easiest to implement. What I expect I'll end up with is a simple although wildly inefficient architecture. Then perhaps we can optimize this toy ISA based on real-world code generation. At the end of the day, however, my goal is...
var handle = _malloc(8); // Make space on the heap to store GL context attributes that need to be accessible as shared between threads. assert(handle, 'malloc() failed in GL.registerContext!'); var context = { handle: handle,
x; if (i < n) y[i] = a*x[i] + y[i]; } int main(void) { int N = 20 * (1 << 20); float *x, *y, *d_x, *d_y; x = (float*)malloc(N*sizeof(float)); y = (float*)malloc(N*sizeof(float)); cudaMalloc(&d_x, N*sizeof(float)); cudaMalloc(&d_y, N*...
This type of kernel is easy to implement but usually only suitable for algorithms that rely on very straightforward data parallelism. The first parameter of the template function (parallel_for) is the range. The range can be of 1, 2, or 3 dimensions. Instances of the range class a...