ROOT   Reference Guide
df022_useKahan.C File Reference

## Detailed Description

Implement a custom action that evaluates a Kahan sum.

This tutorial shows how to implement a Kahan summation custom action.

template <typename T>
class KahanSum final : public ROOT::Detail::RDF::RActionImpl<class KahanSum<T>> {
public:
/// This type is a requirement for every helper.
using Result_t = T;
private:
std::vector<T> fPartialSums;
std::vector<T> fCompensations;
int fNSlots;
std::shared_ptr<T> fResultSum;
void KahanAlgorithm(const T &x, T &sum, T &compensation){
T y = x - compensation;
T t = sum + y;
compensation = (t - sum) - y;
sum = t;
}
public:
KahanSum(KahanSum &&) = default;
KahanSum(const KahanSum &) = delete;
KahanSum(const std::shared_ptr<T> &r) : fResultSum(r)
{
static_assert(std::is_floating_point<T>::value, "Kahan sum makes sense only on floating point numbers");
fPartialSums.resize(fNSlots, 0.);
fCompensations.resize(fNSlots, 0.);
}
std::shared_ptr<Result_t> GetResultPtr() const { return fResultSum; }
void Initialize() {}
void Exec(unsigned int slot, T x)
{
KahanAlgorithm(x, fPartialSums[slot], fCompensations[slot]);
}
void Exec(unsigned int slot, const T &vs)
{
for (auto &&v : vs) {
Exec(slot, v);
}
}
void Finalize()
{
T sum(0) ;
T compensation(0);
for (int i = 0; i < fNSlots; ++i) {
KahanAlgorithm(fPartialSums[i], sum, compensation);
}
*fResultSum = sum;
}
std::string GetActionName() { return "KahanSum"; }
};
void df022_useKahan()
{
// We enable implicit parallelism
auto dd = d.Define("x", "(rdfentry_ %2 == 0) ? 0.00000001 : 100000000.");
auto ptr = std::make_shared<double>();
KahanSum<double> helper(ptr);
auto kahanResult = dd.Book<double>(std::move(helper), {"x"});
auto plainResult = dd.Sum<double>({"x"});
std::cout << std::setprecision(24) << "Kahan: " << *kahanResult << " Classical: " << *plainResult << std::endl;
// Outputs: Kahan: 1000000000.00000011920929 Classical: 1000000000
}
#define d(i)
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