Steve Bronder SteveBronder

Note on Sparse Matrices in Stan

Below we compare the cost of how we currently handle compressed data in Stan vs. how we would handle compressed data if Stan had a sparse matrix type.

The stan model below is a relatively common model form where we are calculating coefficients for several groups.

data {
  int N = 50,000; // # of rows
  int N_1 = 1000; // # of coefficients in group 1

Sparse Matrices, they are good!@

Below we compare the cost of how we currently handle compressed data in Stan vs. how we would handle compressed data if Stan had a sparse matrix type.

The stan model below is a relatively common model form where we are calculating coefficients for several groups.

data {
  int N = 50,000; // # of rows
  int N_1 = 1000; // # of coefficients in group 1

	#include <cstdio>
	#include <stdexcept>
	#include <sstream>
	#include <string>
	#include <typeinfo>
	#include <sstream>
	#include <stdexcept>
	#include <benchmark/benchmark.h>
	#ifdef __GNUC__
	#define likely(x) __builtin_expect(!!(x), 1)

	#include <benchmark/benchmark.h>
	#include <stan/math.hpp>
	#include <utility>
	#include "toss_me.hpp"

	template <typename T1, typename T2>
	static void multiply_vector_vector(benchmark::State& state) {
	using stan::math::var;
	using stan::math::promote_scalar;
	Eigen::MatrixXd x_val = Eigen::VectorXd::Random(state.range(0));


	// Code generated by stanc 51d1957
	#include <stan/model/model_header.hpp>
	namespace glmr_model_namespace {


	inline void validate_positive_index(const char* var_name, const char* expr,
	int val) {
	if (val < 1) {
	std::stringstream msg;

	#ifndef STAN_MATH_REV_FUN_AENERALIZED_INVERSE_HPP
	#define STAN_MATH_REV_FUN_AENERALIZED_INVERSE_HPP

	auto f = [](const auto& a) {
	return stan::math::generalized_inverse(a);
	};
	Eigen::MatrixXd A = Eigen::MatrixXd::Random(10, 10);
	test_ad(f, A);
	#include <stan/math/rev/core.hpp>
	#include <stan/math/prim/err.hpp>

	make: 'test/unit/math/mix/fun/acos_test' is up to date.
	Running main() from lib/benchmark_1.5.1/googletest/googletest/src/gtest_main.cc
	[==========] Running 2 tests from 2 test suites.
	[----------] Global test environment set-up.
	[----------] 1 test from mathMixMatFun
	[ RUN ] mathMixMatFun.acos
	[ OK ] mathMixMatFun.acos (356 ms)
	[----------] 1 test from mathMixMatFun (356 ms total)

	[----------] 1 test from MathMixMatFun

	In file included from test/prob/poisson/poisson_00000_generated_ffd_test.cpp:3:
	In file included from ./test/prob/test_fixture_distr.hpp:4:
	In file included from ./stan/math/mix.hpp:6:
	In file included from ./stan/math/rev.hpp:9:
	In file included from ./stan/math/prim.hpp:12:
	In file included from ./stan/math/prim/prob.hpp:276:
	In file included from ./stan/math/prim/prob/poisson_log.hpp:5:
	./stan/math/prim/prob/poisson_lpmf.hpp:68:28: error: no matching function for call to 'sum'
	T_partials_return logp = stan::math::sum(multiply_log(n_val, lambda_val));
	^~~~~~~~~~~~~~~


	// Code generated by stanc 3ce704a2
	#include <stan/model/model_header.hpp>
	namespace bernoulli_model_namespace {

	inline void validate_positive_index(const char* var_name, const char* expr,
	int val) {
	if (val < 1) {
	std::stringstream msg;
	msg << "Found dimension size less than one in simplex declaration"