-/* Copyright (c) 2004-2014. The SimGrid Team.
+/* Copyright (c) 2004-2015. The SimGrid Team.
* All rights reserved. */
/* This program is free software; you can redistribute it and/or modify it
#ifndef _SURF_MAXMIN_H
#define _SURF_MAXMIN_H
-#include "portable.h"
+#include "src/portable.h"
#include "xbt/misc.h"
#include "xbt/asserts.h"
#include "surf/datatypes.h"
#include <math.h>
-//#include <float.h>
/** @addtogroup SURF_lmm
* @details
- * A linear maxmin solver to resolves inequations systems.
+ * A linear maxmin solver to resolve inequations systems.
*
* Most SimGrid model rely on a "fluid/steady-state" modeling that
- * samount to share resources between actions at relatively
+ * simulate the sharing of resources between actions at relatively
* coarse-grain. Such sharing is generally done by solving a set of
* linear inequations. Let's take an example and assume we have the
* variables \f$x_1\f$, \f$x_2\f$, \f$x_3\f$, and \f$x_4\f$ . Let's
* say that \f$x_1\f$ and \f$x_2\f$ correspond to activities running
- * and the same CPU \f$A\f$ whose capacity is \f$C_A\f$ . In such a
+ * and the same CPU \f$A\f$ whose capacity is \f$C_A\f$. In such a
* case, we need to enforce:
*
* \f[ x_1 + x_2 \leq C_A \f]
* \f$\sum_if(x_i)\f$, where \f$f\f$ is a strictly increasing concave
* function.
*
+ *
* Constraint:
* - bound (set)
* - shared (set)
* - usage (computed)
+ *
* Variable:
* - weight (set)
* - bound (set)
* - value (computed)
+ *
* Element:
* - value (set)
*
* var1.weight * var1.value * elem1.value + var2.weight * var2.value * elem2.value <= cons1.bound
* var2.weight * var2.value * elem3.value + var3.weight * var3.value * elem4.value <= cons2.bound
*
- * where `var1.value`, `var2.value` and `var3.value` are the unknown values
+ * where `var1.value`, `var2.value` and `var3.value` are the unknown values.
+ *
+ * If a constraint is not shared, the sum is replaced by a max.
+ * For example, a third non-shared constraint `cons3` and the associated elements `elem5` and `elem6` could write as:
+ *
+ * max( var1.weight * var1.value * elem5.value , var3.weight * var3.value * elem6.value ) <= cons3.bound
+ *
+ * This is usefull for the sharing of resources for various models.
+ * For instance, for the network model, each link is associated
+ * to a constraint and each communication to a variable.
+ *
+ *
+ * Implementation details
+ *
+ * For implementation reasons, we are interested in distinguishing variables that actually participate to the computation of constraints, and those who are part of the equations but are stuck to zero.
+ * We call enabled variables, those which var.weight is strictly positive. Zero-weight variables are called disabled variables.
+ * Unfortunately this concept of enabled/disabled variables intersects with active/inactive variable.
+ * Semantically, the intent is similar, but the conditions under which a variable is active is slightly more strict than the conditions for it to be enabled.
+ * A variable is active only if its var.value is non-zero (and, by construction, its var.weight is non-zero).
+ * In general, variables remain disabled after their creation, which often models an initialization phase (e.g. first packet propagating in the network). Then, it is enabled by the corresponding model. Afterwards, the max-min solver (lmm_solve()) activates it when appropriate. It is possible that the variable is again disabled, e.g. to model the pausing of an action.
+ *
+ *
+ * Concurrency limit and maximum
+ *
+ * We call concurrency, the number of variables that can be enabled at any time for each constraint.
+ * From a model perspective, this "concurrency" often represents the number of actions that actually compete for one constraint.
+ * The LMM solver is able to limit the concurrency for each constraint, and to monitor its maximum value.
*
- * if a constraint is not shared the sum is replace by a max
+ * One may want to limit the concurrency of constraints for essentially three reasons:
+ * - Keep LMM system in a size that can be solved (it does not react very well with tens of thousands of variables per constraint)
+ * - Stay within parameters where the fluid model is accurate enough.
+ * - Model serialization effects
+ *
+ * The concurrency limit can also be set to a negative value to disable concurrency limit. This can improve performance slightly.
*
- * Its usefull for the sharing of resources for various models.
- * For instance for the network model the link are associated
- * to consrtaint and the communications to variables.
+ * Overall, each constraint contains three fields related to concurrency:
+ * - concurrency_limit which is the limit enforced by the solver
+ * - concurrency_current which is the current concurrency
+ * - concurrency_maximum which is the observed maximum concurrency
+ *
+ * Variables also have one field related to concurrency: concurrency_share.
+ * In effect, in some cases, one variable is involved multiple times (i.e. two elements) in a constraint.
+ * For example, cross-traffic is modeled using 2 elements per constraint.
+ * concurrency_share formally corresponds to the maximum number of elements that associate the variable and any given constraint.
*/
-extern double sg_maxmin_precision;
-extern double sg_surf_precision;
+XBT_PUBLIC_DATA(double) sg_maxmin_precision;
+XBT_PUBLIC_DATA(double) sg_surf_precision;
static XBT_INLINE void double_update(double *variable, double value, double precision)
{
* @param cnst The constraint to share
* @return 1 if shared, 0 otherwise
*/
-XBT_PUBLIC(int) lmm_constraint_is_shared(lmm_constraint_t cnst);
+XBT_PUBLIC(int) lmm_constraint_sharing_policy(lmm_constraint_t cnst);
/**
* @brief Free a constraint
*/
XBT_PUBLIC(double) lmm_constraint_get_usage(lmm_constraint_t cnst);
+/**
+ * @brief Sets the concurrency limit for this constraint
+ *
+ * @param cnst A constraint
+ * @param concurrency_limit The concurrency limit to use for this constraint
+ */
+XBT_PUBLIC(void) lmm_constraint_concurrency_limit_set(lmm_constraint_t cnst, int concurrency_limit);
+
+
+/**
+ * @brief Reset the concurrency maximum for a given variable (we will update the maximum to reflect constraint evolution).
+ *
+ * @param cnst A constraint
+ *
+*/
+XBT_PUBLIC(void) lmm_constraint_concurrency_maximum_reset(lmm_constraint_t cnst);
+
+
+/**
+ * @brief Get the concurrency maximum for a given variable (which reflects constraint evolution).
+ *
+ * @param cnst A constraint
+ * @return the maximum concurrency of the constraint
+ */
+XBT_PUBLIC(int) lmm_constraint_concurrency_maximum_get(lmm_constraint_t cnst);
+
+
/**
* @brief Create a new Linear MaxMin variable
*
*/
XBT_PUBLIC(double) lmm_variable_getbound(lmm_variable_t var);
+/**
+ * @brief Set the concurrent share of the variable
+ *
+ * @param var A variable
+ * @param concurrency_share The new concurrency share
+ */
+XBT_PUBLIC(void) lmm_variable_concurrency_share_set(lmm_variable_t var, short int concurrency_share);
+
/**
* @brief Remove a variable from a constraint
*
lmm_constraint_t cnst,
lmm_element_t * elem);
+/**
+ * @brief Get a var associated to a constraint
+ * @details Get the first variable of the next variable of elem if elem is not NULL
+ *
+ * @param cnst A constraint
+ * @param elem A element of constraint of the constraint or NULL
+ * @param nextelem A element of constraint of the constraint or NULL, the one after elem
+ * @param numelem parameter representing the number of elements to go
+ *
+ * @return A variable associated to a constraint
+ */
+XBT_PUBLIC(lmm_variable_t) lmm_get_var_from_cnst_safe(lmm_system_t /*sys*/,
+ lmm_constraint_t cnst,
+ lmm_element_t * elem,
+ lmm_element_t * nextelem,
+ int * numelem);
+
/**
* @brief Get the first active constraint of a system
*
