Matricized tensor times Khatri-Rao product (MTTKRP) is a bottleneck operation in various algorithms - such as Alternating Least Squares - for computing sparse tensor factorizations like the Canonical Polyadic Decomposition. Mathematically, mode-1 MTTKRP (for order-3 tensors) can be expressed as $A = B_{(1)} (D \odot C)$, where $A$, $C$, and $D$ are (typically) dense matrices, $B$ is an order-3 tensor (matricizied along the first mode), and $\odot$ denotes the Khatri-Rao product. This operation can also be expressed in index notation as

A(i,j) = B(i,k,l) * D(l,j) * C(k,j)


You can use the taco C++ library to easily and efficiently compute the MTTKRP as demonstrated here:

// On Linux and MacOS, you can compile and run this program like so:
//   g++ -std=c++11 -O3 -DNDEBUG -DTACO -I ../../include -L../../build/lib -ltaco mttkrp.cpp -o mttkrp
//   LD_LIBRARY_PATH=../../build/lib ./mttkrp

#include <random>

#include "taco.h"

using namespace taco;

int main(int argc, char* argv[]) {
std::default_random_engine gen(0);
std::uniform_real_distribution<double> unif(0.0, 1.0);

// Predeclare the storage formats that the inputs and output will be stored as.
// To define a format, you must specify whether each dimension is dense or
// sparse and (optionally) the order in which dimensions should be stored. The
// formats declared below correspond to compressed sparse fiber (csf) and
// row-major dense (rm).
Format csf({Sparse,Sparse,Sparse});
Format  rm({Dense,Dense});

// Load a sparse order-3 tensor from file (stored in the FROSTT format) and
// store it as a compressed sparse fiber tensor. The tensor in this example

// Generate a random dense matrix and store it in row-major (dense) format.
// Matrices correspond to order-2 tensors in taco.
Tensor<double> C({B.getDimension(1), 25}, rm);
for (int i = 0; i < C.getDimension(0); ++i) {
for (int j = 0; j < C.getDimension(1); ++j) {
C.insert({i,j}, unif(gen));
}
}
C.pack();

// Generate another random dense matrix and store it in row-major format.
Tensor<double> D({B.getDimension(2), 25}, rm);
for (int i = 0; i < D.getDimension(0); ++i) {
for (int j = 0; j < D.getDimension(1); ++j) {
D.insert({i,j}, unif(gen));
}
}
D.pack();

// Declare the output matrix to be a dense matrix with 25 columns and the same
// number of rows as the number of slices along the first dimension of input
// tensor B, to be also stored as a row-major dense matrix.
Tensor<double> A({B.getDimension(0), 25}, rm);

// Define the MTTKRP computation using index notation.
IndexVar i, j, k, l;
A(i,j) = B(i,k,l) * D(l,j) * C(k,j);

// At this point, we have defined how entries in the output matrix should be
// computed from entries in the input tensor and matrices but have not actually
// performed the computation yet. To do so, we must first tell taco to generate
// code that can be executed to compute the MTTKRP operation.
A.compile();

// We can now call the functions taco generated to assemble the indices of the
// output matrix and then actually compute the MTTKRP.
A.assemble();
A.compute();

// Write the output of the computation to file (stored in the FROSTT format).
write("A.tns", A);
}


Under the hood, when you run the above C++ program, taco generates the imperative code shown below to compute the MTTKRP. taco is able to evaluate this compound operation efficiently with a single kernel that avoids materializing the intermediate Khatri-Rao product.

for (int B1_pos = B.d1.pos[0]; B1_pos < B.d1.pos[(0 + 1)]; B1_pos++) {
int iB = B.d1.idx[B1_pos];
for (int B2_pos = B.d2.pos[B1_pos]; B2_pos < B.d2.pos[(B1_pos + 1)]; B2_pos++) {
int kB = B.d2.idx[B2_pos];
for (int B3_pos = B.d3.pos[B2_pos]; B3_pos < B.d3.pos[(B2_pos + 1)]; B3_pos++) {
int lB = B.d3.idx[B3_pos];
double t37 = B.vals[B3_pos];
for (int jD = 0; jD < 25; jD++) {
int D2_pos = (lB * 25) + jD;
int C2_pos = (kB * 25) + jD;
int A2_pos = (iB * 25) + jD;
A.vals[A2_pos] = A.vals[A2_pos] + ((t37 * D.vals[D2_pos]) * C.vals[C2_pos]);
}
}
}
}