test-chain-load.hpp

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/*
  TEST-CHAIN-LOAD.hpp  -  produce a configurable synthetic computation load

  Copyright (C)         Lumiera.org
    2023,               Hermann Vosseler <Ichthyostega@web.de>

  This program is free software; you can redistribute it and/or
  modify it under the terms of the GNU General Public License as
  published by the Free Software Foundation; either version 2 of
  the License, or (at your option) any later version.

  This program is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with this program; if not, write to the Free Software
  Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.

*/

#ifndef VAULT_GEAR_TEST_TEST_CHAIN_LOAD_H
#define VAULT_GEAR_TEST_TEST_CHAIN_LOAD_H


#include "vault/common.hpp"
#include "lib/test/transiently.hpp"

#include "vault/gear/job.h"
#include "vault/gear/scheduler.hpp"
#include "vault/gear/special-job-fun.hpp"
#include "lib/uninitialised-storage.hpp"
#include "lib/test/microbenchmark.hpp"
#include "lib/incidence-count.hpp"
#include "lib/time/timevalue.hpp"
#include "lib/time/quantiser.hpp"
#include "lib/iter-explorer.hpp"
#include "lib/format-string.hpp"
#include "lib/format-cout.hpp"
#include "lib/random-draw.hpp"
#include "lib/dot-gen.hpp"
#include "lib/util.hpp"

#include <boost/functional/hash.hpp>
#include <functional>
#include <utility>
#include <future>
#include <memory>
#include <string>
#include <vector>
#include <tuple>
#include <array>


namespace vault{
namespace gear {
namespace test {
  
  using util::_Fmt;
  using util::min;
  using util::max;
  using util::isnil;
  using util::limited;
  using util::unConst;
  using util::toString;
  using util::isLimited;
  using util::showHashLSB;
  using lib::time::Time;
  using lib::time::TimeValue;
  using lib::time::FrameRate;
  using lib::time::Duration;
  using lib::test::benchmarkTime;
  using lib::test::microBenchmark;
  using lib::test::Transiently;
  using lib::meta::_FunRet;

  using std::string;
  using std::function;
  using std::make_pair;
  using std::forward;
  using std::string;
  using std::swap;
  using std::move;
  using std::chrono_literals::operator ""s;
  
  namespace err = lumiera::error;
  namespace dot = lib::dot_gen;
  
  namespace { // Default definitions for structured load testing
    
    const size_t DEFAULT_FAN = 16;                      
    const size_t DEFAULT_SIZ = 256;                     
    
    const auto SAFETY_TIMEOUT = 5s;                     
    const auto STANDARD_DEADLINE = 30ms;                
    const size_t DEFAULT_CHUNKSIZE = 64;                
    const double UPFRONT_PLANNING_BOOST = 2.6;          
    const size_t GRAPH_BENCHMARK_RUNS = 5;              
    const size_t LOAD_BENCHMARK_RUNS = 500;             
    const double LOAD_SPEED_BASELINE = 100;             
    const microseconds LOAD_DEFAULT_TIME = 100us;       
    const size_t LOAD_DEFAULT_MEM_SIZE   = 1000;        
    const Duration SCHEDULE_LEVEL_STEP{_uTicks(1ms)};   
    const Duration SCHEDULE_NODE_STEP{Duration::NIL};   
    const Duration SCHEDULE_PLAN_STEP{_uTicks(100us)};  
    const Offset   SCHEDULE_WAKE_UP{_uTicks(10us)};     
    const bool     SCHED_DEPENDS = false;               
    const bool     SCHED_NOTIFY  = true;                
    
    inline uint
    defaultConcurrency()
    {
      return work::Config::getDefaultComputationCapacity();
    }
    
    inline double
    _uSec (microseconds ticks)
    {
      return std::chrono::duration<double, std::micro>{ticks}.count();
    }
  }
  
  struct LevelWeight
    {
      size_t level{0};
      size_t nodes{0};
      size_t endidx{0};
      size_t weight{0};
    };
  
  inline double
  computeWeightFactor (LevelWeight const& lw, uint concurrency)
  {
    REQUIRE (0 < concurrency);
    double speedUp = lw.nodes? lw.nodes / std::ceil (double(lw.nodes)/concurrency)
                             : 1.0;
    ENSURE (1.0 <= speedUp);
    return lw.weight / speedUp;
  }
  
  struct Statistic;
  
  
  
  
  /***********************************************************************/
  template<size_t maxFan =DEFAULT_FAN>
  class TestChainLoad
    : util::MoveOnly
    {
          
    public:
      struct Node
        : util::MoveOnly
        {
          using _Arr = std::array<Node*, maxFan>;
          using Iter = typename _Arr::iterator;
          using CIter = typename _Arr::const_iterator;
          
          struct Tab : _Arr
            {
              Iter after = _Arr::begin();
              
              Iter  end()      { return after; }
              CIter end() const{ return after; }
              friend Iter  end (Tab      & tab){ return tab.end(); }
              friend CIter end (Tab const& tab){ return tab.end(); }
              
              Node* front() { return empty()? nullptr : _Arr::front(); }
              Node* back()  { return empty()? nullptr : *(after-1);    }
              
              void clear() { after = _Arr::begin(); }      
              
              size_t size() const { return unConst(this)->end()-_Arr::begin(); }
              bool  empty() const { return 0 == size();     }
              
              Iter
              add(Node* n)
                {
                  if (after != _Arr::end())
                    {
                      *after = n;
                      return after++;
                    }
                  NOTREACHED ("excess node linkage");
                }
              
            };
          
          size_t hash;
          size_t level{0}, weight{0};
          Tab pred{0}, succ{0};
          
          Node(size_t seed =0)
            : hash{seed}
            { }
          
          void
          clear()
            {
              hash = 0;
              level = weight = 0;
              pred.clear();
              succ.clear();
            }
          
          Node&
          addPred (Node* other)
            {
              REQUIRE (other);
              pred.add (other);
              other->succ.add (this);
              return *this;
            }
          
          Node&
          addSucc (Node* other)
            {
              REQUIRE (other);
              succ.add (other);
              other->pred.add (this);
              return *this;
            }
          Node& addPred(Node& other) { return addPred(&other); }
          Node& addSucc(Node& other) { return addSucc(&other); }
          
          size_t
          calculate()
            {
              for (Node* entry: pred)
                boost::hash_combine (hash, entry->hash);
              return hash;
            }
          
          friend bool isStart (Node const& n) { return isnil (n.pred); };
          friend bool isExit  (Node const& n) { return isnil (n.succ); };
          friend bool isInner (Node const& n) { return not (isStart(n) or isExit(n)); }
          friend bool isFork  (Node const& n) { return 1 < n.succ.size(); }
          friend bool isJoin  (Node const& n) { return 1 < n.pred.size(); }
          friend bool isLink  (Node const& n) { return 1 == n.pred.size() and 1 == n.succ.size(); }
          friend bool isKnot  (Node const& n) { return isFork(n) and isJoin(n); }

          
          friend bool isStart (Node const* n) { return n and isStart(*n); };
          friend bool isExit  (Node const* n) { return n and isExit (*n); };
          friend bool isInner (Node const* n) { return n and isInner(*n); };
          friend bool isFork  (Node const* n) { return n and isFork (*n); };
          friend bool isJoin  (Node const* n) { return n and isJoin (*n); };
          friend bool isLink  (Node const* n) { return n and isLink (*n); };
          friend bool isKnot  (Node const* n) { return n and isKnot (*n); };
        };
        
        
        class NodeControlBinding;
        
        using Param = lib::Limited<size_t, maxFan>;
        
        using Rule = lib::RandomDraw<NodeControlBinding>;
      
    private:
      using NodeTab = typename Node::Tab;
      using NodeIT = lib::RangeIter<Node*>;
      
      std::unique_ptr<Node[]> nodes_;
      size_t numNodes_;
      
      Rule seedingRule_  {};
      Rule expansionRule_{};
      Rule reductionRule_{};
      Rule pruningRule_  {};
      Rule weightRule_   {};
      
      Node* frontNode() { return &nodes_[0];          }
      Node* afterNode() { return &nodes_[numNodes_];  }
      Node* backNode()  { return &nodes_[numNodes_-1];}
      
    public:
      explicit
      TestChainLoad(size_t nodeCnt =DEFAULT_SIZ)
        : nodes_{new Node[nodeCnt]}
        , numNodes_{nodeCnt}
        {
          REQUIRE (1 < nodeCnt);
        }
      
      
      size_t size()     const { return numNodes_; }
      size_t topLevel() const { return unConst(this)->backNode()->level; }
      size_t getSeed()  const { return unConst(this)->frontNode()->hash; }
      
      
      auto
      allNodes()
        {
          return lib::explore (NodeIT{frontNode(),afterNode()});
        }
      auto
      allNodePtr()
        {
          return allNodes().asPtr();
        }
      
      auto
      allExitNodes()
        {
          return allNodes().filter([](Node& n){ return isExit(n); });
        }
      auto
      allExitHashes()  const
        {
          return unConst(this)->allExitNodes().transform([](Node& n){ return n.hash; });
        }
      
      size_t
      getHash()  const
        {
          auto combineBoostHashes = [](size_t h, size_t hx){ boost::hash_combine(h,hx); return h;};
          return allExitHashes()
                    .filter([](size_t h){ return h != 0; })
                    .reduce(lib::iter_explorer::IDENTITY
                           ,combineBoostHashes
                           );
        }
      
      
      size_t nodeID(Node const* n){ return n - frontNode(); };
      size_t nodeID(Node const& n){ return nodeID (&n); };
      
      
      
      /* ===== topology control ===== */
      
      TestChainLoad&&
      seedingRule (Rule r)
        {
          seedingRule_ = move(r);
          return move(*this);
        }
      
      TestChainLoad&&
      expansionRule (Rule r)
        {
          expansionRule_ = move(r);
          return move(*this);
        }
      
      TestChainLoad&&
      reductionRule (Rule r)
        {
          reductionRule_ = move(r);
          return move(*this);
        }
      
      TestChainLoad&&
      pruningRule (Rule r)
        {
          pruningRule_ = move(r);
          return move(*this);
        }
      
      TestChainLoad&&
      weightRule (Rule r)
        {
          weightRule_ = move(r);
          return move(*this);
        }
      
      
      static Rule rule() { return Rule(); }
      static Rule value(size_t v) { return Rule().fixedVal(v); }
      
      static Rule
      rule_atStart (uint v)
        {
          return Rule().mapping([v](Node* n)
                                  {
                                    return isStart(n)? Rule().fixedVal(v)
                                                     : Rule();
                                  });
        }
      
      static Rule
      rule_atJoin (uint v)
        {
          return Rule().mapping([v](Node* n)
                                  {
                                    return isJoin(n) ? Rule().fixedVal(v)
                                                     : Rule();
                                  });
        }
      
      static Rule
      rule_atLink (uint v)
        {
          return Rule().mapping([v](Node* n)
                                  { // NOTE: when applying these rules,
                                    //       successors are not yet wired...
                                    return not (isJoin(n) or isStart(n))
                                                     ? Rule().fixedVal(v)
                                                     : Rule();
                                  });
        }
      
      static Rule
      rule_atJoin_else (double p1, double p2, uint v=1)
        {
          return Rule().mapping([p1,p2,v](Node* n)
                                  {
                                    return isJoin(n) ? Rule().probability(p1).maxVal(v)
                                                     : Rule().probability(p2).maxVal(v);
                                  });
        }
      
      
      TestChainLoad&&
      configure_isolated_nodes()
        {
          pruningRule(value(1));
          weightRule(value(1));
          return move(*this);
        }
      
      TestChainLoad&&
      configureShape_short_chains2()
        {
          pruningRule(rule().probability(0.8));
          weightRule(value(1));
          return move(*this);
        }
      
      TestChainLoad&&
      configureShape_short_chains3_interleaved()
        {
          pruningRule(rule().probability(0.6));
          seedingRule(rule_atStart(1));
          weightRule(value(1));
          return move(*this);
        }
      
      TestChainLoad&&
      configureShape_short_segments3_interleaved()
        {
          seedingRule(rule().probability(0.8).maxVal(1));
          reductionRule(rule().probability(0.75).maxVal(3));
          pruningRule(rule_atJoin(1));
          weightRule(value(1));
          return move(*this);
        }
      
      TestChainLoad&&
      configureShape_chain_loadBursts()
        {
          expansionRule(rule().probability(0.27).maxVal(4));
          reductionRule(rule().probability(0.44).maxVal(6).minVal(2));
          weightRule   (rule().probability(0.66).maxVal(3));
          setSeed(55);        // ◁─────── produces a prelude with parallel chains,
          return move(*this);//           then fork at level 17 followed by bursts of load.
        }

      
      
      TestChainLoad&&
      buildTopology()
        {
          NodeTab a,b,          // working data for generation
                 *curr{&a},     // the current set of nodes to carry on
                 *next{&b};     // the next set of nodes connected to current
          Node* node = frontNode();
          size_t level{0};
          
          // transient snapshot of rules (non-copyable, once engaged)
          Transiently originalExpansionRule{expansionRule_};
          Transiently originalReductionRule{reductionRule_};
          Transiently originalseedingRule  {seedingRule_};
          Transiently originalPruningRule  {pruningRule_};
          Transiently originalWeightRule   {weightRule_};
          
          // prepare building blocks for the topology generation...
          auto moreNext  = [&]{ return next->size() < maxFan;      };
          auto moreNodes = [&]{ return node <= backNode();         };
          auto spaceLeft = [&]{ return moreNext() and moreNodes(); };
          auto addNode   = [&](size_t seed =0)
                              {
                                Node* n = *next->add (node++);
                                n->clear();
                                n->level = level;
                                n->hash = seed;
                                return n;
                              };
          auto apply  = [&](Rule& rule, Node* n)
                              {
                                return rule(n);
                              };
          auto calcNode = [&](Node* n)
                              {
                                n->calculate();
                                n->weight = apply(weightRule_,n);
                              };
          
          // visit all further nodes and establish links
          while (moreNodes())
            {
              curr->clear();
              swap (next, curr);
              size_t toReduce{0};
              Node* r = nullptr;
              REQUIRE (spaceLeft());
              for (Node* o : *curr)
                { // follow-up on all Nodes in current level...
                  calcNode(o);
                  if (apply (pruningRule_,o))
                    continue; // discontinue
                  size_t toSeed   = apply (seedingRule_, o);
                  size_t toExpand = apply (expansionRule_,o);
                  while (0 < toSeed and spaceLeft())
                    { // start a new chain from seed
                      addNode(this->getSeed());
                      --toSeed;
                    }
                  while (0 < toExpand and spaceLeft())
                    { // fork out secondary chain from o
                      Node* n = addNode();
                      o->addSucc(n);
                      --toExpand;
                    }
                  if (not toReduce)
                    {          // carry-on chain from o
                      r = spaceLeft()? addNode():nullptr;
                      toReduce = apply (reductionRule_, o);
                    }
                  else
                    --toReduce;
                  if (r) // connect chain from o...
                    r->addPred(o);
                  else // space for successors is already exhausted
                    { //  can not carry-on, but must ensure no chain is broken
                      ENSURE (not next->empty());
                      if (o->succ.empty())
                        o->addSucc (next->back());
                    }
                }
              ENSURE (not isnil(next) or spaceLeft());
              if (isnil(next)) // ensure graph continues
                addNode(this->getSeed());
              ENSURE (not next->empty());
              ++level;
            }
          ENSURE (node > backNode());
          // all open nodes on last level become exit nodes
          for (Node* o : *next)
              calcNode(o);
          //
          return move(*this);
        }
      
      
      TestChainLoad&&
      setSeed (size_t seed = rand())
        {
          frontNode()->hash = seed;
          return move(*this);
        }
      
      
      TestChainLoad&&
      setWeight (size_t fixedNodeWeight =1)
        {
          for (Node& n : allNodes())
            n.weight = fixedNodeWeight;
          return move(*this);
        }
      
      
      TestChainLoad&&
      recalculate()
        {
          size_t seed = this->getSeed();
          for (Node& n : allNodes())
            {
              n.hash = isStart(n)? seed : 0;
              n.calculate();
            }
          return move(*this);
        }
      
      
      TestChainLoad&&
      clearNodeHashes()
        {
          size_t seed = this->getSeed();
          for (Node& n : allNodes())
            n.hash = isStart(n)? seed : 0;
          return move(*this);
        }
      
      
      
      /* ===== Operators ===== */
      
      std::string
      generateTopologyDOT()
        {
          using namespace dot;
          
          Section nodes("Nodes");
          Section layers("Layers");
          Section topology("Topology");
          
          // Styles to distinguish the computation nodes
          Code BOTTOM{"shape=doublecircle"};
          Code SEED  {"shape=circle"};
          Code TOP   {"shape=box, style=rounded"};
          Code DEFAULT{};
          
          // prepare time-level zero
          size_t level(0);
          auto timeLevel = scope(level).rank("min ");
          
          for (Node& n : allNodes())
            {
              size_t i = nodeID(n);
              string tag{toString(i)+": "+showHashLSB(n.hash)};
              if (n.weight) tag +="."+toString(n.weight);
              nodes += node(i).label(tag)
                              .style(i==0         ? BOTTOM
                                    :isnil(n.pred)? SEED
                                    :isnil(n.succ)? TOP
                                    :               DEFAULT);
              for (Node* suc : n.succ)
                topology += connect (i, nodeID(*suc));
              
              if (level != n.level)
                {// switch to next time-level
                  layers += timeLevel;
                  ++level;
                  ENSURE (level == n.level);
                  timeLevel = scope(level).rank("same");
                }
              timeLevel.add (node(i));
            }
          layers += timeLevel; // close last layer
          
          // combine and render collected definitions as DOT-code
          return digraph (nodes, layers, topology);
        }
      
      TestChainLoad&&
      printTopologyDOT()
        {
          cout << "───═══───═══───═══───═══───═══───═══───═══───═══───═══───═══───\n"
               << generateTopologyDOT()
               << "───═══───═══───═══───═══───═══───═══───═══───═══───═══───═══───"
               << endl;
          return move(*this);
        }
      
      
      double
      calcRuntimeReference(microseconds timeBase =LOAD_DEFAULT_TIME
                          ,size_t       sizeBase =0
                          ,size_t       repeatCnt=GRAPH_BENCHMARK_RUNS
                          )
        {
          return microBenchmark ([&]{ performGraphSynchronously(timeBase,sizeBase); }
                                ,repeatCnt)
                              .first; // ∅ runtime in µs
        }
      
      TestChainLoad&& performGraphSynchronously(microseconds timeBase =LOAD_DEFAULT_TIME
                                               ,size_t       sizeBase =0);
      
      TestChainLoad&&
      printRuntimeReference(microseconds timeBase =LOAD_DEFAULT_TIME
                           ,size_t       sizeBase =0
                           ,size_t       repeatCnt=GRAPH_BENCHMARK_RUNS
                           )
        {
          cout << _Fmt{"runtime ∅(%d) = %6.2fms   (single-threaded)\n"}
                      % repeatCnt
                      % (1e-3 * calcRuntimeReference(timeBase,sizeBase,repeatCnt))
               << "───═══───═══───═══───═══───═══───═══───═══───═══───═══───═══───"
               << endl;
          return move(*this);
        }
      
      
      size_t
      calcWeightSum()
        {
          return allNodes()
                  .transform([](Node& n){ return n.weight; })
                  .resultSum();
        }
      
      auto
      allLevelWeights()
        {
          return allNodes()
                  .groupedBy([](Node& n){ return n.level; }
                            ,[this](LevelWeight& lw, Node const& n)
                              {
                                lw.level = n.level;
                                lw.weight += n.weight;
                                lw.endidx = nodeID(n);
                                ++lw.nodes;
                              }
                            );
        }
      
      auto
      levelScheduleSequence (uint concurrency =1)
        {
          return allLevelWeights()
                  .transform([schedule=0.0, concurrency]
                             (LevelWeight const& lw) mutable
                              {
                                schedule += computeWeightFactor (lw, concurrency);
                                return schedule;
                              });
        }
      
      double
      calcExpectedCompoundedWeight (uint concurrency =1)
        {
          return allLevelWeights()
                  .transform([concurrency](LevelWeight const& lw){ return computeWeightFactor (lw, concurrency); })
                  .resultSum();
        }
      
      
      
      Statistic computeGraphStatistics();
      TestChainLoad&& printTopologyStatistics();
      
      class ScheduleCtx;
      friend class ScheduleCtx; // accesses raw storage array
      
      ScheduleCtx setupSchedule (Scheduler& scheduler);
    };
  
  
  
  template<size_t maxFan>
  class TestChainLoad<maxFan>::NodeControlBinding
    : public std::function<Param(Node*)>
    {
    protected:
      static size_t
      defaultSrc (Node* node)
        {
          return node? node->hash:0;
        }
      
      static size_t
      level (Node* node)
        {
          return node? node->level:0;
        }
      
      static double
      guessHeight (size_t level)
        {     // heuristic guess for a »fully stable state«
          double expectedHeight = 2*maxFan;
          return level / expectedHeight;
        }
      
      
      
      template<class SIG>
      struct Adaptor
        {
          static_assert (not sizeof(SIG), "Unable to adapt given functor.");
        };
      
      template<typename RES>
      struct Adaptor<RES(size_t)>
        {
          template<typename FUN>
          static auto
          build (FUN&& fun)
            {
              return [functor=std::forward<FUN>(fun)]
                     (Node* node) -> _FunRet<FUN>
                        {
                          return functor (defaultSrc (node));
                        };
            }
        };
      
      template<typename RES>
      struct Adaptor<RES(size_t,double)>
        {
          
          template<typename FUN>
          static auto
          build (FUN&& fun)
            {
              return [functor=std::forward<FUN>(fun)]
                     (Node* node) -> _FunRet<FUN>
                        {
                          return functor (defaultSrc (node)
                                         ,guessHeight(level(node)));
                        };
            }
        };
      
      template<typename RES>
      struct Adaptor<RES(double)>
        {
          
          template<typename FUN>
          static auto
          build (FUN&& fun)
            {
              return [functor=std::forward<FUN>(fun)]
                     (Node* node) -> _FunRet<FUN>
                        {
                          return functor (guessHeight(level(node)));
                        };
            }
        };
    };
  
  
  
  
  /* ========= Graph Statistics Evaluation ========= */
  
  struct StatKey
    : std::pair<size_t,string>
    {
      using std::pair<size_t,string>::pair;
      operator size_t const&() const { return this->first; }
      operator string const&() const { return this->second;}
    };
  const StatKey STAT_NODE{0,"node"};    
  const StatKey STAT_SEED{1,"seed"};    
  const StatKey STAT_EXIT{2,"exit"};    
  const StatKey STAT_INNR{3,"innr"};    
  const StatKey STAT_FORK{4,"fork"};    
  const StatKey STAT_JOIN{5,"join"};    
  const StatKey STAT_LINK{6,"link"};    
  const StatKey STAT_KNOT{7,"knot"};    
  const StatKey STAT_WGHT{8,"wght"};    
  
  const std::array KEYS = {STAT_NODE,STAT_SEED,STAT_EXIT,STAT_INNR,STAT_FORK,STAT_JOIN,STAT_LINK,STAT_KNOT,STAT_WGHT};
  const uint CAT = KEYS.size();
  const uint IDX_SEED = 1; // index of STAT_SEED
  
  namespace {
    template<class NOD>
    inline auto
    prepareEvaluations()
    {
      return std::array<std::function<uint(NOD&)>, CAT>
        { [](NOD&  ){ return 1;         }
        , [](NOD& n){ return isStart(n);}
        , [](NOD& n){ return isExit(n); }
        , [](NOD& n){ return isInner(n);}
        , [](NOD& n){ return isFork(n); }
        , [](NOD& n){ return isJoin(n); }
        , [](NOD& n){ return isLink(n); }
        , [](NOD& n){ return isKnot(n); }
        , [](NOD& n){ return n.weight;  }
        };
    }
  }
  
  using VecU      = std::vector<uint>;
  using LevelSums = std::array<uint, CAT>;
  
  struct Indicator
    {
      VecU data{};
      uint cnt{0};                   
      double frac{0};                
      double pS {0};                 
      double pL {0};                 
      double pLW{0};                 
      double cL {0};                 
      double cLW{0};                 
      double sL {0};                 
      double sLW{0};                 
      
      void
      addPoint (uint levelID, uint sublevelID, uint width, uint items)
        {
          REQUIRE (levelID == data.size()); // ID is zero based
          REQUIRE (width > 0);
          data.push_back (items);
          cnt += items;
          pS  += items;
          pL  += items;
          pLW += items / double(width);
          cL  += levelID * items;
          cLW += levelID * items/double(width);
          sL  += sublevelID * items;
          sLW += sublevelID * items/double(width);
        }
      
      void
      closeAverages (uint nodes, uint levels, uint segments, double avgheight)
        {
          REQUIRE (levels == data.size());
          REQUIRE (levels > 0);
          frac = cnt / double(nodes);
          cL   = pL?   cL/pL  :0;    // weighted averages: normalise to weight sum
          cLW  = pLW? cLW/pLW :0;
          sL   = pL?   sL/pL  :0;
          sLW  = pLW? sLW/pLW :0;
          pS  /= segments;           // simple averages : normalise to number of levels
          pL  /= levels;             // simple averages : normalise to number of levels
          pLW /= levels;
          cL  /= levels-1;           // weight centres : as fraction of maximum level-ID
          cLW /= levels-1;
          ASSERT (avgheight >= 1.0);
          if (avgheight > 1.0)
            {                        // likewise for weight centres relative to subgraph
              sL  /= avgheight-1;    // height is 1-based, while the contribution was 0-based
              sLW /= avgheight-1;
            }
          else
              sL = sLW = 0.5;
        }
    };
  
  struct Statistic
    {
      uint nodes{0};
      uint levels{0};
      uint segments{1};
      uint maxheight{0};
      double avgheight{0};
      VecU width{};
      VecU sublevel{};
      
      std::array<Indicator, CAT> indicators;
      
      explicit
      Statistic (uint lvls)
        : nodes{0}
        , levels{0}
        {
          reserve (lvls);
        }
      
      void
      addPoint (uint levelWidth, uint sublevelID, LevelSums& particulars)
        {
          levels += 1;
          nodes += levelWidth;
          width.push_back (levelWidth);
          sublevel.push_back (sublevelID);
          ASSERT (levels == width.size());
          ASSERT (0 < levels);
          ASSERT (0 < levelWidth);
          for (uint i=0; i< CAT; ++i)
            indicators[i].addPoint (levels-1, sublevelID, levelWidth, particulars[i]);
        }
      
      void
      closeAverages (uint segs, uint maxSublevelID)
        {
          segments = segs;
          maxheight = maxSublevelID + 1;
          avgheight = levels / double(segments);
          for (uint i=0; i< CAT; ++i)
            indicators[i].closeAverages (nodes,levels,segments,avgheight);
        }
      
    private:
      void
      reserve (uint lvls)
        {
          width.reserve (lvls);
          sublevel.reserve(lvls);
          for (uint i=0; i< CAT; ++i)
            {
              indicators[i] = Indicator{};
              indicators[i].data.reserve(lvls);
            }
        }
    };
  
  
  
  template<size_t maxFan>
  inline Statistic
  TestChainLoad<maxFan>::computeGraphStatistics()
    {
      auto totalLevels = uint(topLevel());
      auto classify = prepareEvaluations<Node>();
      Statistic stat(totalLevels);
      LevelSums particulars{0};
      size_t level{0},
          sublevel{0},
       maxsublevel{0};
      size_t segs{0};
      uint width{0};
      auto detectSubgraphs = [&]{ // to be called when a level is complete
                                  if (width==1 and particulars[IDX_SEED]==1)
                                    { // previous level actually started new subgraph
                                      sublevel = 0;
                                      ++segs;
                                    }
                                  else
                                    maxsublevel = max (sublevel,maxsublevel);
                                };
      
      for (Node& node : allNodes())
        {
          if (level != node.level)
            {// Level completed...
              detectSubgraphs();
              // record statistics for previous level
              stat.addPoint (width, sublevel, particulars);
              //   switch to next time-level
              ++level;
              ++sublevel;
              ENSURE (level == node.level);
              particulars = LevelSums{0};
              width = 0;
            }
           // classify and account..
          ++width;
          for (uint i=0; i<CAT; ++i)
            particulars[i] += classify[i](node);
        }
      ENSURE (level == topLevel());
      detectSubgraphs();
      stat.addPoint (width, sublevel, particulars);
      stat.closeAverages (segs, maxsublevel);
      return stat;
    }
  
  
  
  template<size_t maxFan>
  inline TestChainLoad<maxFan>&&
  TestChainLoad<maxFan>::printTopologyStatistics()
    {
      cout << "INDI: cnt frac   ∅pS  ∅pL  ∅pLW  γL◆ γLW◆  γL⬙ γLW⬙\n";
      _Fmt line{"%4s: %3d %3.0f%% %5.1f %5.2f %4.2f %4.2f %4.2f %4.2f %4.2f\n"};
      Statistic stat = computeGraphStatistics();
      for (uint i=0; i< CAT; ++i)
        {
          Indicator& indi = stat.indicators[i];
          cout << line % KEYS[i]
                       % indi.cnt
                       % (indi.frac*100)
                       % indi.pS
                       % indi.pL
                       % indi.pLW
                       % indi.cL
                       % indi.cLW
                       % indi.sL
                       % indi.sLW
                       ;
        }
      cout << _Fmt{"LEVL: %3d\n"} % stat.levels;
      cout << _Fmt{"SEGS: %3d   h = ∅%3.1f / max.%2d\n"}
                       % stat.segments
                       % stat.avgheight
                       % stat.maxheight;
      cout << "───═══───═══───═══───═══───═══───═══───═══───═══───═══───═══───"
           << endl;
      return move(*this);
    }
  
  
  
  
  
  /* ========= Configurable Computational Load ========= */
  
  class ComputationalLoad
    : util::MoveAssign
    {
      using Sink = volatile size_t;
      
      static double&
      computationSpeed (bool mem)     
        {
          static double cpuSpeed{LOAD_SPEED_BASELINE};
          static double memSpeed{LOAD_SPEED_BASELINE};
          return mem? memSpeed : cpuSpeed;
        }
      
    public:
      microseconds timeBase = LOAD_DEFAULT_TIME;
      size_t       sizeBase = LOAD_DEFAULT_MEM_SIZE;
      bool useAllocation = false;
      
      double
      invoke (uint scaleStep =1)
        {
          if (scaleStep == 0 or timeBase < 1us)
            return 0;  //  disabled
          return useAllocation? benchmarkTime ([this,scaleStep]{ causeMemProcessLoad (scaleStep); })
                              : benchmarkTime ([this,scaleStep]{ causeComputationLoad(scaleStep); });
        }
      
      double
      benchmark (uint scaleStep =1)
        {
          return microBenchmark ([&]{ invoke(scaleStep);}
                                ,LOAD_BENCHMARK_RUNS)
                              .first; // ∅ runtime in µs
        }
      
      void
      calibrate()
        {
          TRANSIENTLY(useAllocation) = false;
          performIncrementalCalibration();
          useAllocation = true;
          performIncrementalCalibration();
        }
      
      void
      maybeCalibrate()
        {
          if (not isCalibrated())
            calibrate();
        }
      
      bool
      isCalibrated()  const
        {
          return computationSpeed(false) != LOAD_SPEED_BASELINE;
        }
      
    private:
      uint64_t
      roundsNeeded (uint scaleStep)
        {
          auto desiredMicros = scaleStep*timeBase.count();
          return uint64_t(desiredMicros*computationSpeed(useAllocation));
        }
      
      auto
      allocNeeded (uint scaleStep)
        {
          auto cnt = roundsNeeded(scaleStep);
          auto siz = max (scaleStep * sizeBase, 1u);
          auto rep = max (cnt/siz, 1u);
          // increase size to fit
          siz = cnt / rep;
          return make_pair (siz,rep);
        }
      
      void
      causeComputationLoad (uint scaleStep)
        {
          auto round = roundsNeeded (scaleStep);
          Sink sink;
          size_t scree{sink};
          for ( ; 0 < round; --round)
            boost::hash_combine (scree,scree);
          sink = scree;
          sink++;
        }
      
      void
      causeMemProcessLoad (uint scaleStep)
        {
          auto [siz,round] = allocNeeded (scaleStep);
          lib::UninitialisedDynBlock<size_t> memBlock{siz};
          Sink sink;
          *memBlock.front() = sink+1;
          for ( ; 0 < round; --round)
            for (size_t i=0; i<memBlock.size()-1; ++i)
              memBlock[i+1] += memBlock[i];
          sink = *memBlock.back();
          sink++;
        }
      
      double
      determineSpeed()
        {
          uint step4gauge = 1;
          double micros   = benchmark (step4gauge);
          auto stepsDone  = roundsNeeded (step4gauge);
          return stepsDone / micros;
        }
      
      void
      performIncrementalCalibration()
        {
          double& speed = computationSpeed(useAllocation);
          double prev{speed},delta;
          do {
              speed = determineSpeed();
              delta = abs(1.0 - speed/prev);
              prev = speed;
            }
          while (delta > 0.05);
        }
    };
  
  
  inline SpecialJobFun
  onetimeCrunch (milliseconds runTime)
  {   // ensure calibration prior to use
    ComputationalLoad().maybeCalibrate();
    //
    return SpecialJobFun{
            [runTime](JobParameter) -> void
                        {
                          ComputationalLoad crunch;
                          crunch.timeBase = runTime;
                          crunch.invoke();
                        }};
  }
  
  
  
  
  template<size_t maxFan>
  TestChainLoad<maxFan>&&
  TestChainLoad<maxFan>::performGraphSynchronously (microseconds timeBase, size_t sizeBase)
  {
    ComputationalLoad compuLoad;
    compuLoad.timeBase = timeBase;
    if (not sizeBase)
      {
        compuLoad.sizeBase = LOAD_DEFAULT_MEM_SIZE;
        compuLoad.useAllocation =false;
      }
    else
      {
        compuLoad.sizeBase = sizeBase;
        compuLoad.useAllocation =true;
      }
    compuLoad.maybeCalibrate();
    
    size_t seed = this->getSeed();
    for (Node& n : allNodes())
      {
        n.hash = isStart(n)? seed : 0;
        if (n.weight)
          compuLoad.invoke (n.weight);
        n.calculate();
      }
    return move(*this);
  }

  
  
  
  
  /* ========= Render Job generation and Scheduling ========= */
  
  class ChainFunctor
    : public JobClosure
    {
      
      static lib::time::Grid&
      testGrid()        
        {
          static lib::time::FixedFrameQuantiser gridOne{FrameRate::STEP};
          return gridOne;
        }
      
      /* === JobFunctor Interface === */
      
      string diagnostic()  const             =0;
      void invokeJobOperation (JobParameter) =0;
      
      JobKind
      getJobKind()  const
        {
          return TEST_JOB;
        }
      
      InvocationInstanceID
      buildInstanceID (HashVal)  const override
        {
          return InvocationInstanceID();
        }
      
      size_t
      hashOfInstance (InvocationInstanceID invoKey)  const override
        {
          std::hash<size_t> hashr;
          HashVal res = hashr (invoKey.frameNumber);
          return res;
        }
      
    public:
      
      static InvocationInstanceID
      encodeNodeID (size_t idx)
        {
          InvocationInstanceID invoKey;
          invoKey.code.w1 = idx;
          return invoKey;
        };
      
      static size_t
      decodeNodeID (InvocationInstanceID invoKey)
        {
          return size_t(invoKey.code.w1);
        };
      
      static Time
      encodeLevel (size_t level)
        {
          return Time{testGrid().timeOf (FrameCnt(level))};
        }
      
      static size_t
      decodeLevel (TimeValue nominalTime)
        {
          return testGrid().gridPoint (nominalTime);
        }
    };
  
  
  
  template<size_t maxFan>
  class RandomChainCalcFunctor
    : public ChainFunctor
    {
      using Node = typename TestChainLoad<maxFan>::Node;
      using Watch = lib::IncidenceCount;
      
      Node* startNode_;
      ComputationalLoad* compuLoad_;
      Watch* watch_;
      
    public:
      RandomChainCalcFunctor(Node& startNode, ComputationalLoad* load =nullptr, Watch* watch =nullptr)
        : startNode_{&startNode}
        , compuLoad_{load}
        , watch_{watch}
        { }
      
      
      void
      invokeJobOperation (JobParameter param)  override
        {
          if (watch_) watch_->markEnter();
          size_t nodeIdx = decodeNodeID (param.invoKey);
          size_t level = decodeLevel (TimeValue{param.nominalTime});
          Node& target = startNode_[nodeIdx];
          ASSERT (target.level == level);
          // invoke the »media calculation«
          if (compuLoad_ and target.weight)
            compuLoad_->invoke (target.weight);
          target.calculate();
          if (watch_) watch_->markLeave();
        }
      
      string diagnostic()  const override
        {
          return _Fmt{"ChainCalc(w:%d)◀%s"}
                     % maxFan
                     % util::showAddr(startNode_);
        }
    };
  
  
  template<size_t maxFan>
  class RandomChainPlanFunctor
    : public ChainFunctor
    {
      using Node = typename TestChainLoad<maxFan>::Node;
      
      function<void(size_t,size_t)>          scheduleCalcJob_;
      function<void(Node*,Node*)>             markDependency_;
      function<void(size_t,size_t,size_t,bool)> continuation_;
      
      size_t maxCnt_;
      Node* nodes_;
      
      size_t currIdx_{0}; // Note: this test-JobFunctor is statefull
      
    public:
      template<class CAL, class DEP, class CON>
      RandomChainPlanFunctor(Node& nodeArray, size_t nodeCnt,
                             CAL&& schedule, DEP&& markDepend,
                             CON&& continuation)
        : scheduleCalcJob_{forward<CAL> (schedule)}
        , markDependency_{forward<DEP> (markDepend)}
        , continuation_{forward<CON> (continuation)}
        , maxCnt_{nodeCnt}
        , nodes_{&nodeArray}
        { }
      
      
      void
      invokeJobOperation (JobParameter param)  override
        {
          size_t start{currIdx_};
          size_t reachedLevel{0};
          size_t targetNodeIDX = decodeNodeID (param.invoKey);
          for ( ; currIdx_<maxCnt_; ++currIdx_)
            {
              Node* n = &nodes_[currIdx_];
              if (currIdx_ <= targetNodeIDX)
                reachedLevel = n->level;
              else // continue until end of current level
                if (n->level > reachedLevel)
                  break;
              scheduleCalcJob_(currIdx_, n->level);
              for (Node* pred: n->pred)
                markDependency_(pred,n);
            }
          ENSURE (currIdx_ > 0);
          continuation_(start, currIdx_-1, reachedLevel, currIdx_ < maxCnt_);
        }
      
      
      string diagnostic()  const override
        {
          return "ChainPlan";
        }
    };
  
  
  
  
  template<size_t maxFan>
  class TestChainLoad<maxFan>::ScheduleCtx
    : util::MoveOnly
    {
      TestChainLoad& chainLoad_;
      Scheduler&     scheduler_;
      
      lib::UninitialisedDynBlock<ScheduleSpec> schedule_;

      FrameRate  levelSpeed_{1, SCHEDULE_LEVEL_STEP};
      FrameRate   planSpeed_{1, SCHEDULE_PLAN_STEP};
      TimeVar   nodeExpense_{SCHEDULE_NODE_STEP};
      double      stressFac_{1.0};
      bool      schedNotify_{SCHED_NOTIFY};
      bool     schedDepends_{SCHED_DEPENDS};
      uint     blockLoadFac_{2};
      size_t      chunkSize_{DEFAULT_CHUNKSIZE};
      TimeVar     startTime_{Time::ANYTIME};
      microseconds deadline_{STANDARD_DEADLINE};
      microseconds  preRoll_{guessPlanningPreroll()};
      ManifestationID manID_{};
      
      std::vector<TimeVar> startTimes_{};
      std::promise<void> signalDone_{};
      
      std::unique_ptr<ComputationalLoad>              compuLoad_;
      std::unique_ptr<RandomChainCalcFunctor<maxFan>> calcFunctor_;
      std::unique_ptr<RandomChainPlanFunctor<maxFan>> planFunctor_;
      
      std::unique_ptr<lib::IncidenceCount> watchInvocations_;
      
      
      /* ==== Callbacks from job planning ==== */
      
      void
      disposeStep (size_t idx, size_t level)
        {
          schedule_[idx] = scheduler_.defineSchedule(calcJob (idx,level))
                                     .manifestation(manID_)
                                     .startTime (jobStartTime(level, idx))
                                     .lifeWindow (deadline_);
          Node& n = chainLoad_.nodes_[idx];
          if (isnil (n.pred)
              or schedDepends_)
            schedule_[idx].post();
        }// Node with dependencies will be triggered by NOTIFY
        //  and thus must not necessarily be scheduled explicitly.
      
      void
      setDependency (Node* pred, Node* succ)
        {
          size_t predIdx = chainLoad_.nodeID (pred);
          size_t succIdx = chainLoad_.nodeID (succ);
          bool unlimitedTime = not schedNotify_;
          schedule_[predIdx].linkToSuccessor (schedule_[succIdx], unlimitedTime);
        }
      
      void
      continuation (size_t chunkStart, size_t lastNodeIDX, size_t levelDone, bool work_left)
        {
          if (work_left)
            {
              size_t nextChunkEndNode = calcNextChunkEnd (lastNodeIDX);
              scheduler_.continueMetaJob (calcPlanScheduleTime (lastNodeIDX+1)
                                         ,planningJob (nextChunkEndNode)
                                         ,manID_);
            }
          else
            {
              auto wakeUp =  scheduler_.defineSchedule(wakeUpJob())
                                       .manifestation (manID_)
                                       .startTime(jobStartTime (levelDone+1, lastNodeIDX+1) + SCHEDULE_WAKE_UP)
                                       .lifeWindow(SAFETY_TIMEOUT)
                                       .post();
              for (size_t exitIDX : lastExitNodes (chunkStart))
                wakeUp.linkToPredecessor (schedule_[exitIDX]);
            }      //  Setup wait-dependency on last computations
        }
      
      
      std::future<void>
      performRun()
        {
          auto finished = attachNewCompletionSignal();
          size_t numNodes = chainLoad_.size();
          size_t firstChunkEndNode = calcNextChunkEnd(0);
          schedule_.allocate (numNodes);
          compuLoad_->maybeCalibrate();
          calcFunctor_.reset (new RandomChainCalcFunctor<maxFan>{chainLoad_.nodes_[0], compuLoad_.get(), watchInvocations_.get()});
          planFunctor_.reset (new RandomChainPlanFunctor<maxFan>{chainLoad_.nodes_[0], chainLoad_.numNodes_
                                                                ,[this](size_t i, size_t l){ disposeStep(i,l);  }
                                                                ,[this](auto* p, auto* s)  { setDependency(p,s);}
                                                                ,[this](size_t s,size_t n,size_t l, bool w)
                                                                                       { continuation(s,n,l,w); }
                                                                });
          startTime_ = anchorSchedule();
          scheduler_.seedCalcStream (planningJob(firstChunkEndNode)
                                    ,manID_
                                    ,calcLoadHint());
          return finished;
        }
      
      
    public:
      ScheduleCtx (TestChainLoad& mother, Scheduler& scheduler)
        : chainLoad_{mother}
        , scheduler_{scheduler}
        , compuLoad_{new ComputationalLoad}
        , calcFunctor_{}
        , planFunctor_{}
        { }
      
      double
      launch_and_wait()
        try {
            return benchmarkTime ([this]
                                  {
                                    awaitBlocking(
                                      performRun());
                                  })
                 -_uSec(preRoll_); // timing accounted without pre-roll
          }
          ERROR_LOG_AND_RETHROW(test,"Scheduler testing")
      
      auto
      getScheduleSeq()
        {
          if (isnil (startTimes_))
            fillDefaultSchedule();
          
          return lib::explore(startTimes_)
                     .transform([&](Time jobTime) -> TimeVar
                                  {
                                    return jobTime - startTimes_.front();
                                  });
        }
      
      double
      getExpectedEndTime()
        {
          return _raw(startTimes_.back() - startTimes_.front()
                     + Duration{nodeExpense_}*(chainLoad_.size()/stressFac_));
        }
      
      auto
      getInvocationStatistic()
        {
          return watchInvocations_? watchInvocations_->evaluate()
                                  : lib::IncidenceCount::Statistic{};
        }
      
      double
      calcRuntimeReference()
        {
          microseconds timeBase = compuLoad_->timeBase;
          size_t sizeBase = compuLoad_->useAllocation? compuLoad_->sizeBase : 0;
          return chainLoad_.calcRuntimeReference (timeBase, sizeBase);
        }
      
      double getStressFac() { return stressFac_; }
      
      
      
      /* ===== Setter / builders for custom configuration ===== */
      
      ScheduleCtx&&
      withInstrumentation (bool doWatch =true)
        {
          if (doWatch)
            {
              watchInvocations_.reset (new lib::IncidenceCount);
              watchInvocations_->expectThreads (work::Config::COMPUTATION_CAPACITY)
                                .expectIncidents(chainLoad_.size());
            }
          else
            watchInvocations_.reset();
          return move(*this);
        }
      
      ScheduleCtx&&
      withPlanningStep (microseconds planningTime_per_node)
        {
          planSpeed_ = FrameRate{1, Duration{_uTicks(planningTime_per_node)}};
          preRoll_ = guessPlanningPreroll();
          return move(*this);
        }
      
      ScheduleCtx&&
      withChunkSize (size_t nodes_per_chunk)
        {
          chunkSize_ = nodes_per_chunk;
          preRoll_ = guessPlanningPreroll();
          return move(*this);
        }
      
      ScheduleCtx&&
      withPreRoll (microseconds planning_headstart)
        {
          preRoll_ = planning_headstart;
          return move(*this);
        }
      
      ScheduleCtx&&
      withUpfrontPlanning()
        {
          withChunkSize (chainLoad_.size());
          preRoll_ *= UPFRONT_PLANNING_BOOST;
          return move(*this);
        }
      
      ScheduleCtx&&
      withLevelDuration (microseconds fixedTime_per_level)
        {
          levelSpeed_ = FrameRate{1, Duration{_uTicks(fixedTime_per_level)}};
          return move(*this);
        }
      
      ScheduleCtx&&
      withBaseExpense (microseconds fixedTime_per_node)
        {
          nodeExpense_ = _uTicks(fixedTime_per_node);
          return move(*this);
        }
      
      ScheduleCtx&&
      withSchedDepends (bool explicitly)
        {
          schedDepends_ = explicitly;
          return move(*this);
        }
      
      ScheduleCtx&&
      withSchedNotify (bool doSetTime =true)
        {
          schedNotify_ = doSetTime;
          return move(*this);
        }
      
      ScheduleCtx&&
      withAdaptedSchedule (double stressFac =1.0, uint concurrency=0, double formFac =1.0)
        {
          if (not concurrency)  // use hardware concurrency (#cores) by default
            concurrency = defaultConcurrency();
          ENSURE (isLimited (1u, concurrency, 3*defaultConcurrency()));
          REQUIRE (formFac > 0.0);
          stressFac /= formFac;
          withLevelDuration (compuLoad_->timeBase);
          fillAdaptedSchedule (stressFac, concurrency);
          return move(*this);
        }
      
      double
      determineEmpiricFormFactor (uint concurrency=0)
        {
          if (not watchInvocations_) return 1.0;
          auto stat = watchInvocations_->evaluate();
          if (0 == stat.activationCnt) return 1.0;
          // looks like we have actual measurement data...
          ENSURE (0.0 < stat.avgConcurrency);
          if (not concurrency)
            concurrency = defaultConcurrency();
          double worktimeRatio = 1 - stat.timeAtConc(0) / stat.coveredTime;
          double workConcurrency = stat.avgConcurrency / worktimeRatio;
          double weightSum = chainLoad_.calcWeightSum();
          double expectedCompoundedWeight = chainLoad_.calcExpectedCompoundedWeight(concurrency);
          double expectedConcurrency = weightSum / expectedCompoundedWeight;
          double formFac = 1 / (workConcurrency / expectedConcurrency);
          double expectedNodeTime = _uSec(compuLoad_->timeBase) * weightSum / chainLoad_.size();
          double realAvgNodeTime = stat.activeTime / stat.activationCnt;
          formFac *= realAvgNodeTime / expectedNodeTime;
          return formFac;
        }
      
      ScheduleCtx&&
      withJobDeadline (microseconds deadline_after_start)
        {
          deadline_ = deadline_after_start;
          return move(*this);
        }
      
      ScheduleCtx&&
      withAnnouncedLoadFactor (uint factor_on_levelSpeed)
        {
          blockLoadFac_ = factor_on_levelSpeed;
          return move(*this);
        }
      
      ScheduleCtx&&
      withManifestation (ManifestationID manID)
        {
          manID_ = manID;
          return move(*this);
        }
      
      ScheduleCtx&&
      withLoadTimeBase (microseconds timeBase =LOAD_DEFAULT_TIME)
        {
          compuLoad_->timeBase = timeBase;
          return move(*this);
        }
      
      ScheduleCtx&&
      deactivateLoad()
        {
          compuLoad_->timeBase = 0us;
          return move(*this);
        }
      
      ScheduleCtx&&
      withLoadMem (size_t sizeBase =LOAD_DEFAULT_MEM_SIZE)
        {
          if (not sizeBase)
            {
              compuLoad_->sizeBase = LOAD_DEFAULT_MEM_SIZE;
              compuLoad_->useAllocation =false;
            }
          else
            {
              compuLoad_->sizeBase = sizeBase;
              compuLoad_->useAllocation =true;
            }
          return move(*this);
        }
      
    private:
      std::future<void>
      attachNewCompletionSignal()
        {
          std::promise<void> notYetTriggered;
          signalDone_.swap (notYetTriggered);
          return signalDone_.get_future();
        }
      
      void
      awaitBlocking(std::future<void> signal)
        {
          if (std::future_status::timeout == signal.wait_for (SAFETY_TIMEOUT))
            throw err::Fatal("Timeout on Scheduler test exceeded.");
        }
      
      Job
      calcJob (size_t idx, size_t level)
        {
          return Job{*calcFunctor_
                    , calcFunctor_->encodeNodeID(idx)
                    , calcFunctor_->encodeLevel(level)
                    };
        }
      
      Job
      planningJob (size_t endNodeIDX)
        {
          return Job{*planFunctor_
                    , planFunctor_->encodeNodeID(endNodeIDX)
                    , Time::ANYTIME
                    };
        }
      
      Job
      wakeUpJob ()
        {
          SpecialJobFun wakeUpFun{[this](JobParameter)
                                    {
                                      signalDone_.set_value();
                                    }};
          return Job{ wakeUpFun
                    , InvocationInstanceID()
                    , Time::ANYTIME
                    };
        }
      
      microseconds
      guessPlanningPreroll()
        {
          return microseconds(_raw(Time{chunkSize_ / planSpeed_}));
        }
      
      FrameRate
      calcLoadHint()
        {
          return FrameRate{levelSpeed_ * blockLoadFac_};
        }
      
      size_t
      calcNextChunkEnd (size_t lastNodeIDX)
        {
          lastNodeIDX += chunkSize_;
          return min (lastNodeIDX, chainLoad_.size()-1);
        }                      //  prevent out-of-bound access
      
      Time
      anchorSchedule()
        {
          Time anchor = RealClock::now() + _uTicks(preRoll_);
          if (isnil (startTimes_))
            fillDefaultSchedule();
          size_t numPoints = chainLoad_.topLevel()+2;
          ENSURE (startTimes_.size() == numPoints);
          Offset base{startTimes_.front(), anchor};
          for (size_t level=0; level<numPoints; ++level)
            startTimes_[level] += base;
          return anchor;
        }
      
      void
      fillDefaultSchedule()
        {
          size_t numPoints = chainLoad_.topLevel()+2;
          stressFac_ = 1.0;
          startTimes_.clear();
          startTimes_.reserve (numPoints);
          for (size_t level=0; level<numPoints; ++level)
            startTimes_.push_back (level / levelSpeed_);
        }
      
      void
      fillAdaptedSchedule (double stressFact, uint concurrency)
        {
          REQUIRE (stressFact > 0);
          stressFac_ = stressFact;
          size_t numPoints = chainLoad_.topLevel()+2;
          startTimes_.clear();
          startTimes_.reserve (numPoints);
          startTimes_.push_back (Time::ZERO);
          chainLoad_.levelScheduleSequence (concurrency)
                    .transform([&](double scheduleFact){ return (scheduleFact/stressFac_) * Offset{1,levelSpeed_};})
                    .effuse(startTimes_);
        }
      
      Time
      jobStartTime (size_t level, size_t nodeIDX =0)
        {
          ENSURE (level < startTimes_.size());
          return startTimes_[level]
               + nodeExpense_ * (nodeIDX/stressFac_);
        }
      
      auto
      lastExitNodes (size_t lastChunkStartIDX)
        {
          return chainLoad_.allExitNodes()
                           .transform([&](Node& n){ return chainLoad_.nodeID(n); })
                           .filter([=](size_t idx){ return idx >= lastChunkStartIDX; });
        }                // index of all Exit-Nodes within last planning-chunk...
      
      Time
      calcPlanScheduleTime (size_t lastNodeIDX)
        {/* must be at least 1 level ahead,
            because dependencies are defined backwards;
            the chain-load graph only defines dependencies over one level
            thus the first level in the next chunk must still be able to attach
            dependencies to the last row of the preceding chunk, implying that
            those still need to be ahead of schedule, and not yet dispatched.
          */
          lastNodeIDX = min (lastNodeIDX, chainLoad_.size()-1);  // prevent out-of-bound access
          size_t nextChunkLevel = chainLoad_.nodes_[lastNodeIDX].level;
          nextChunkLevel = nextChunkLevel>2? nextChunkLevel-2 : 0;
          return jobStartTime(nextChunkLevel) - _uTicks(preRoll_);
        }
    };
  
  
  template<size_t maxFan>
  typename TestChainLoad<maxFan>::ScheduleCtx
  TestChainLoad<maxFan>::setupSchedule (Scheduler& scheduler)
  {
    clearNodeHashes();
    return ScheduleCtx{*this, scheduler};
  }
  
  
  
}}} // namespace vault::gear::test
#endif /*VAULT_GEAR_TEST_TEST_CHAIN_LOAD_H*/