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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
#define EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
namespace Eigen {
/** \class TensorEvaluator
* \ingroup CXX11_Tensor_Module
*
* \brief A cost model used to limit the number of threads used for evaluating
* tensor expression.
*
*/
// Class storing the cost of evaluating a tensor expression in terms of the
// estimated number of operand bytes loads, bytes stored, and compute cycles.
class TensorOpCost {
public:
// TODO(rmlarsen): Fix the scalar op costs in Eigen proper. Even a simple
// model based on minimal reciprocal throughput numbers from Intel or
// Agner Fog's tables would be better than what is there now.
template <typename ArgType>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int MulCost() {
return internal::functor_traits<
internal::scalar_product_op<ArgType, ArgType> >::Cost;
}
template <typename ArgType>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int AddCost() {
return internal::functor_traits<internal::scalar_sum_op<ArgType> >::Cost;
}
template <typename ArgType>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int DivCost() {
return internal::functor_traits<
internal::scalar_quotient_op<ArgType, ArgType> >::Cost;
}
template <typename ArgType>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int ModCost() {
return internal::functor_traits<internal::scalar_mod_op<ArgType> >::Cost;
}
template <typename SrcType, typename TargetType>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int CastCost() {
return internal::functor_traits<
internal::scalar_cast_op<SrcType, TargetType> >::Cost;
}
EIGEN_DEVICE_FUNC
TensorOpCost() : bytes_loaded_(0), bytes_stored_(0), compute_cycles_(0) {}
EIGEN_DEVICE_FUNC
TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles)
: bytes_loaded_(bytes_loaded),
bytes_stored_(bytes_stored),
compute_cycles_(compute_cycles) {}
EIGEN_DEVICE_FUNC
TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles,
bool vectorized, double packet_size)
: bytes_loaded_(bytes_loaded),
bytes_stored_(bytes_stored),
compute_cycles_(vectorized ? compute_cycles / packet_size
: compute_cycles) {
eigen_assert(bytes_loaded >= 0 && (numext::isfinite)(bytes_loaded));
eigen_assert(bytes_stored >= 0 && (numext::isfinite)(bytes_stored));
eigen_assert(compute_cycles >= 0 && (numext::isfinite)(compute_cycles));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_loaded() const {
return bytes_loaded_;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_stored() const {
return bytes_stored_;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double compute_cycles() const {
return compute_cycles_;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double total_cost(
double load_cost, double store_cost, double compute_cost) const {
return load_cost * bytes_loaded_ + store_cost * bytes_stored_ +
compute_cost * compute_cycles_;
}
// Drop memory access component. Intended for cases when memory accesses are
// sequential or are completely masked by computations.
EIGEN_DEVICE_FUNC void dropMemoryCost() {
bytes_loaded_ = 0;
bytes_stored_ = 0;
}
// TODO(rmlarsen): Define min in terms of total cost, not elementwise.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMin(
const TensorOpCost& rhs) const {
double bytes_loaded = numext::mini(bytes_loaded_, rhs.bytes_loaded());
double bytes_stored = numext::mini(bytes_stored_, rhs.bytes_stored());
double compute_cycles = numext::mini(compute_cycles_, rhs.compute_cycles());
return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles);
}
// TODO(rmlarsen): Define max in terms of total cost, not elementwise.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMax(
const TensorOpCost& rhs) const {
double bytes_loaded = numext::maxi(bytes_loaded_, rhs.bytes_loaded());
double bytes_stored = numext::maxi(bytes_stored_, rhs.bytes_stored());
double compute_cycles = numext::maxi(compute_cycles_, rhs.compute_cycles());
return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator+=(
const TensorOpCost& rhs) {
bytes_loaded_ += rhs.bytes_loaded();
bytes_stored_ += rhs.bytes_stored();
compute_cycles_ += rhs.compute_cycles();
return *this;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator*=(double rhs) {
bytes_loaded_ *= rhs;
bytes_stored_ *= rhs;
compute_cycles_ *= rhs;
return *this;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator+(
TensorOpCost lhs, const TensorOpCost& rhs) {
lhs += rhs;
return lhs;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*(
TensorOpCost lhs, double rhs) {
lhs *= rhs;
return lhs;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*(
double lhs, TensorOpCost rhs) {
rhs *= lhs;
return rhs;
}
friend std::ostream& operator<<(std::ostream& os, const TensorOpCost& tc) {
return os << "[bytes_loaded = " << tc.bytes_loaded()
<< ", bytes_stored = " << tc.bytes_stored()
<< ", compute_cycles = " << tc.compute_cycles() << "]";
}
private:
double bytes_loaded_;
double bytes_stored_;
double compute_cycles_;
};
// TODO(rmlarsen): Implement a policy that chooses an "optimal" number of theads
// in [1:max_threads] instead of just switching multi-threading off for small
// work units.
template <typename Device>
class TensorCostModel {
public:
// Scaling from Eigen compute cost to device cycles.
static const int kDeviceCyclesPerComputeCycle = 1;
// Costs in device cycles.
static const int kStartupCycles = 100000;
static const int kPerThreadCycles = 100000;
static const int kTaskSize = 40000;
// Returns the number of threads in [1:max_threads] to use for
// evaluating an expression with the given output size and cost per
// coefficient.
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int numThreads(
double output_size, const TensorOpCost& cost_per_coeff, int max_threads) {
double cost = totalCost(output_size, cost_per_coeff);
double threads = (cost - kStartupCycles) / kPerThreadCycles + 0.9;
// Make sure we don't invoke undefined behavior when we convert to an int.
threads = numext::mini<double>(threads, GenericNumTraits<int>::highest());
return numext::mini(max_threads,
numext::maxi<int>(1, static_cast<int>(threads)));
}
// taskSize assesses parallel task size.
// Value of 1.0 means ideal parallel task size. Values < 1.0 mean that task
// granularity needs to be increased to mitigate parallelization overheads.
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double taskSize(
double output_size, const TensorOpCost& cost_per_coeff) {
return totalCost(output_size, cost_per_coeff) / kTaskSize;
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double totalCost(
double output_size, const TensorOpCost& cost_per_coeff) {
// Cost of memory fetches from L2 cache. 64 is typical cache line size.
// 11 is L2 cache latency on Haswell.
// We don't know whether data is in L1, L2 or L3. But we are most interested
// in single-threaded computational time around 100us-10ms (smaller time
// is too small for parallelization, larger time is not interesting
// either because we are probably using all available threads already).
// And for the target time range, L2 seems to be what matters. Data set
// fitting into L1 is too small to take noticeable time. Data set fitting
// only into L3 presumably will take more than 10ms to load and process.
const double kLoadCycles = 1.0 / 64 * 11;
const double kStoreCycles = 1.0 / 64 * 11;
// Scaling from Eigen compute cost to device cycles.
return output_size *
cost_per_coeff.total_cost(kLoadCycles, kStoreCycles,
kDeviceCyclesPerComputeCycle);
}
};
} // namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
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