see from outside of compiler

Compiler is always fun when it accompanies with computer architecture. The research on programming system (& language) is what I want to do besides CAD/EDA and mathematics. Not sure it is related to my A+ score in programming language course in graduate school (sorry for the self-obsession :b).

The course I find more focus on implementation is CMU version taught by Prof. Todd C. Mowry

at computer science dept. The course repo is at

The LLVM  is used in the course, which is very handy and important to play around with compiler.

The last page of

shows the references such as writing pass.

For research related stuff, check this 


Implementation mistake causes memory leakage when build Sparse Matrix in mex C/C++ to interact with MATLAB

Let’s first take a look at 6 Lessons From Dropbox – One Million Files Saved Every 15 Minutes, we know, when the product scales, engineers tend to use more computational efficient (C/C++) languages to compensate the side effect of rapid prototyping/development (Python) language.

Similar thing happens in academic prototyping, MATLAB is convenient, and more worthwhile to be used to explore novel idea and save time. However, some important parts are required to implemented in more efficient way to make the idea more solid. Therefore, programming and interacting programming languages is a handy skill set to computer science students.  When we deal with those situation, memory management and transferring is always pain.

In this article, I program in C with MEX to talk to MATLAB. I mistakenly transfer the sparse matrix format with redundant allocations so it blows up the memory (PS: I deal with large scale computing problem).

Do not use this memory Blow-Up version code:

plhs[0] = mxCreateSparse( c_stamp->M, c_stamp->N, c_stamp->NNZ, mxREAL);
cPr = (double *)mxMalloc(sizeof(double) * (c_stamp->NNZ));
cIr = (mwIndex *)mxMalloc(sizeof(mwIndex) * (c_stamp->NNZ));
cJc = (mwIndex *)mxMalloc(sizeof(mwIndex) * (c_stamp->N + 1));

//copy memory
memcpy( cPr, c_stamp->Pr, sizeof(double) * (c_stamp->NNZ));
memcpy( cIr, (mwIndex *)(c_stamp->Ir), sizeof(mwIndex) * (c_stamp->NNZ));
memcpy( cJc, (mwIndex *)(c_stamp->Jc), sizeof(mwIndex) * (c_stamp->N + 1));

mxSetPr(plhs[0], cPr);
mxSetIr(plhs[0], cIr);
mxSetJc(plhs[0], cJc);

Instead, this version is right

plhs[0] = mxCreateSparse( c_stamp->M, c_stamp->N, c_stamp->NNZ, mxREAL);
cPr = (double *) mxGetPr(plhs[0]);
memcpy(cPr, c_stamp->Pr, sizeof(double)*(c_stamp->NNZ));
cIr = (mwIndex *) mxGetIr(plhs[0]);
memcpy( cIr, (mwIndex *)(c_stamp->Ir), sizeof(mwIndex) * (c_stamp->NNZ));
cJc = (mwIndex *) mxGetJc(plhs[0]);
memcpy( cJc, (mwIndex *)(c_stamp->Jc), sizeof(mwIndex) * (c_stamp->N + 1));

where cPr, cIr, cJc are defined before. c_stamp is a sparse matrix I want to wrap from C/C++ to MATLAB via mex.