Row Major vs Column Major Matrices

The Problem

“Row major” and “column major” refer to different memory layouts used to store a matrix. HLSL and GLSL use column major by default, while CPU-side code tends to favor row major. We’ll learn how to reconcile these differences shortly.

Math literature generally expresses matrix transformation with the vector on the left side, oriented vertically, and with the matrices themselves containing vertical vectors defining a transformation. On the other hand, graphics literature often has vectors multiplied on the right side with the vectors of a matrix spanning horizontally. We can call these conventions “column vectors” and “row vectors” respectively, and they are not what we’re talking about when referring to a row or column major layout.

    \[\textit{given vectors }\vec{a}\textit{, }\vec{b}\textit{, }\vec{c}\]

    \[\begin{split} \left(  \begin{array}{c} \left[ \begin{array}{ccc} a_x & a_y & a_z \end{array} \right] \\ \left[ \begin{array}{ccc} b_x & b_y & b_z \end{array} \right] \\ \left[ \begin{array}{ccc} c_x & c_y & c_z \end{array} \right] \end{array}  \right)&  \textit{ row vectors}\\ \left(  \begin{array}{ccc} \left[ \begin{array}{c} a_x \\ b_x \\ c_x \end{array} \right] & \left[ \begin{array}{c} a_y \\ b_y \\ c_y \end{array} \right] & \left[ \begin{array}{c} a_z \\ b_z \\ c_z \end{array} \right] \end{array}  \right)&  \textit{ column vectors}\\ \\ \end{split}\]

A row major matrix is stored sequentially with the elements of the top row first, then the next row, and so on. A column major matrix is stored with the elements down the leftmost column first, then the next column and so forth. It’s tempting to associate these memory layouts with row and column vectors, but don’t do it. We could actually choose to store the matrix any way we want, like “Spiral major” or “checkerboard major” or something else ridiculous. We just have to make sure we modify our matrix functions to read the correct spot in memory when accessing each element.

    \[\textit{given row vectors}\]

    \[ \left(  \begin{array}{c} \left[ \begin{array}{ccc} \boldsymbol{A_x} & \boldsymbol{A_y} & \boldsymbol{A_z} \end{array} \right] \\ \left[ \begin{array}{ccc} b_x & b_y & b_z \end{array} \right] \\ \left[ \begin{array}{ccc} c_x & c_y & c_z \end{array} \right] \end{array}  \right) \]

    \[\begin{split} \left[\begin{array}{ccccccccc} \boldsymbol{A_x} & \boldsymbol{A_y} & \boldsymbol{A_z} & b_x & b_y & b_z & c_x & c_y & c_z \end{array}\right]& \textit{   row major}\\ \left[\begin{array}{ccccccccc} \boldsymbol{A_x} & b_x & c_x & \boldsymbol{A_y} & b_y & c_y & \boldsymbol{A_z} & b_z & c_z \end{array}\right]& \textit{   column major} \end{split}\]


The Tools

In theory, to convert from one layout to another we need a conversion function, but the memory happens to line up such that we can convert a row major matrix to a column major one (or vice versa) just by transposing it. We can also reinterpret a row major matrix to column major by not touching the memory at all and just pretending we have a column major matrix. The effect of doing so will give us a transposed matrix. Our row vectors are now column vectors. If we previously multiplied on the right, we need to multiply on the left. This is true going the other way as well (reinterpreting column major to row major).

When uploading a matrix to shader constants that need conversion, we have three options.

  1. Run the conversion function before uploading, and use the matrix the same way as we did in CPU code.
  2. Upload the matrix as-is and treat it as reinterpreted. Going from row major to column major or vice versa has the effect of transposing the matrix. If we used row vectors before, use column vectors in our shader code (multiply from the right, instead of the left). This has the advantage of no runtime cost.
  3. Explicitly declare our shaders to use the same memory layout as our CPU code. This is a pretty good option, but there are reasons for needing a certain layout in the shader.

These are all the tools necessary to work with mixed row and column major matrices.


A Clarification

There is one bit of confusion that leads people astray, and should be addressed. If you’ve been paying attention to how reinterpretation works, you may notice a row major matrix with row vectors happens to be identical in memory to a column major matrix with column vectors. They are not the same thing. They have different storage formats and different vector orientations, and they need to be treated that way. We are free to reinterpret one to the other, but they are not the same thing.

For more information check out Fabian Giesen’s excellent post. Some of the comments show just how easy it is to confuse the situation when we don’t divorce the concepts of memory layout and vector orientation.