agents

%% quantum_runtime.erl
%% Self-optimizing distributed runtime with advanced patterns
-module(quantum_runtime).
-behaviour(gen_server).

%% API
-export([
    start_link/0,
    start_cluster/1,
    register_agent_cluster/2,
    optimize_execution/2,
    migrate_process/3,
    coordinate_gc/1,
    analyze_patterns/0,
    get_cluster_topology/0
]).

%% gen_server callbacks
-export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).

%% Internal exports
-export([
    pattern_analyzer/1,
    code_optimizer/2,
    numa_scheduler/1,
    gc_coordinator/1,
    thermal_monitor/1
]).

-define(TRACE_TABLE, quantum_traces).
-define(TOPOLOGY_TABLE, numa_topology).
-define(OPTIMIZATION_TABLE, code_optimizations).
-define(CLUSTER_REGISTRY, cluster_registry).

-record(state, {
    cluster_id :: binary(),
    node_topology :: map(),
    execution_patterns :: map(),
    optimization_cache :: map(),
    gc_coordinator :: pid(),
    numa_scheduler :: pid(),
    pattern_analyzer :: pid(),
    thermal_monitor :: pid(),
    distribution_protocol :: atom(),
    quantum_state :: map()
}).

-record(execution_pattern, {
    module :: atom(),
    function :: atom(),
    arity :: integer(),
    call_frequency :: integer(),
    avg_execution_time :: float(),
    memory_usage :: integer(),
    heat_level :: float(),
    numa_affinity :: integer()
}).

-record(numa_node, {
    id :: integer(),
    cpus :: [integer()],
    memory_mb :: integer(),
    load :: float(),
    temperature :: float(),
    processes :: [pid()]
}).

%% ============================================================================
%% API Functions
%% ============================================================================

start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

%% Start a new multi-agent cluster with quantum optimization
start_cluster(ClusterConfig) ->
    ClusterId = generate_cluster_id(),
    
    %% Initialize quantum state for this cluster
    QuantumState = initialize_quantum_state(ClusterConfig),
    
    %% Start cluster nodes with advanced distribution
    Nodes = start_cluster_nodes(ClusterConfig, QuantumState),
    
    %% Initialize cross-cluster coordination
    setup_inter_cluster_coordination(ClusterId, Nodes),
    
    %% Start advanced monitoring and optimization
    start_optimization_subsystems(ClusterId, Nodes),
    
    {ok, ClusterId, Nodes}.

register_agent_cluster(ClusterId, AgentSpecs) ->
    gen_server:call(?MODULE, {register_cluster, ClusterId, AgentSpecs}).

optimize_execution(Module, Function) ->
    gen_server:call(?MODULE, {optimize_execution, Module, Function}).

migrate_process(Pid, TargetNode, Reason) ->
    gen_server:call(?MODULE, {migrate_process, Pid, TargetNode, Reason}).

coordinate_gc(ProcessGroup) ->
    gen_server:call(?MODULE, {coordinate_gc, ProcessGroup}).

analyze_patterns() ->
    gen_server:call(?MODULE, analyze_patterns).

get_cluster_topology() ->
    gen_server:call(?MODULE, get_cluster_topology).

%% ============================================================================
%% gen_server callbacks
%% ============================================================================

init([]) ->
    %% Initialize ETS tables for high-performance operations
    setup_ets_tables(),
    
    %% Detect hardware topology
    NodeTopology = detect_numa_topology(),
    
    %% Initialize quantum coordination state
    QuantumState = #{
        entanglement_pairs => #{},
        coherence_time => 1000,
        decoherence_rate => 0.01,
        quantum_gates => initialize_quantum_gates()
    },
    
    %% Start subsystems
    {ok, PatternAnalyzer} = start_pattern_analyzer(),
    {ok, NumaScheduler} = start_numa_scheduler(NodeTopology),
    {ok, GcCoordinator} = start_gc_coordinator(),
    {ok, ThermalMonitor} = start_thermal_monitor(),
    
    %% Enable advanced tracing
    setup_advanced_tracing(),
    
    State = #state{
        cluster_id = generate_cluster_id(),
        node_topology = NodeTopology,
        execution_patterns = #{},
        optimization_cache = #{},
        gc_coordinator = GcCoordinator,
        numa_scheduler = NumaScheduler,
        pattern_analyzer = PatternAnalyzer,
        thermal_monitor = ThermalMonitor,
        distribution_protocol = quantum_distribution,
        quantum_state = QuantumState
    },
    
    {ok, State}.

handle_call({register_cluster, ClusterId, AgentSpecs}, _From, State) ->
    %% Register new agent cluster with quantum entanglement
    NewState = register_quantum_cluster(ClusterId, AgentSpecs, State),
    {reply, ok, NewState};

handle_call({optimize_execution, Module, Function}, _From, State) ->
    %% Real-time code optimization based on execution patterns
    OptimizedCode = generate_optimized_code(Module, Function, State),
    NewState = update_optimization_cache(Module, Function, OptimizedCode, State),
    {reply, {ok, OptimizedCode}, NewState};

handle_call({migrate_process, Pid, TargetNode, Reason}, _From, State) ->
    %% NUMA-aware process migration with thermal consideration
    Result = execute_numa_migration(Pid, TargetNode, Reason, State),
    {reply, Result, State};

handle_call({coordinate_gc, ProcessGroup}, _From, State) ->
    %% Coordinated garbage collection across process groups
    GcPlan = plan_coordinated_gc(ProcessGroup, State),
    execute_gc_plan(GcPlan, State),
    {reply, ok, State};

handle_call(analyze_patterns, _From, State) ->
    %% Analyze execution patterns for optimization opportunities
    Patterns = analyze_execution_patterns(State),
    {reply, Patterns, State};

handle_call(get_cluster_topology, _From, State) ->
    %% Return current cluster topology with thermal and load data
    Topology = get_enhanced_topology(State),
    {reply, Topology, State};

handle_call(_Request, _From, State) ->
    {reply, {error, unknown_request}, State}.

handle_cast({execution_trace, TraceData}, State) ->
    %% Process execution traces for pattern analysis
    NewState = process_execution_trace(TraceData, State),
    {noreply, NewState};

handle_cast({thermal_update, NodeId, Temperature}, State) ->
    %% Update thermal information for scheduling decisions
    NewState = update_thermal_state(NodeId, Temperature, State),
    {noreply, NewState};

handle_cast({quantum_entanglement, Pid1, Pid2}, State) ->
    %% Establish quantum entanglement between processes
    NewState = create_quantum_entanglement(Pid1, Pid2, State),
    {noreply, NewState};

handle_cast(_Msg, State) ->
    {noreply, State}.

handle_info({trace, Pid, call, {M, F, A}}, State) ->
    %% Handle execution traces for real-time optimization
    TraceData = #{
        pid => Pid,
        module => M,
        function => F,
        arity => length(A),
        timestamp => erlang:monotonic_time(nanosecond),
        scheduler_id => erlang:system_info(scheduler_id)
    },
    NewState = update_execution_patterns(TraceData, State),
    {noreply, NewState};

handle_info({gc_event, Pid, Info}, State) ->
    %% Handle garbage collection events for coordination
    NewState = process_gc_event(Pid, Info, State),
    {noreply, NewState};

handle_info(_Info, State) ->
    {noreply, State}.

terminate(_Reason, _State) ->
    ok.

code_change(_OldVsn, State, _Extra) ->
    {ok, State}.

%% ============================================================================
%% Advanced Pattern Analysis
%% ============================================================================

pattern_analyzer(State) ->
    %% Continuous analysis of execution patterns
    receive
        {analyze, Traces} ->
            Patterns = extract_execution_patterns(Traces),
            HotPaths = identify_hot_paths(Patterns),
            OptimizationOpportunities = find_optimization_opportunities(HotPaths),
            
            %% Generate optimized code for hot paths
            lists:foreach(fun(Opportunity) ->
                generate_and_deploy_optimization(Opportunity)
            end, OptimizationOpportunities),
            
            pattern_analyzer(State);
        stop ->
            ok
    end.

extract_execution_patterns(Traces) ->
    %% Machine learning-based pattern extraction
    TraceGroups = group_traces_by_call_signature(Traces),
    
    maps:map(fun({M, F, A}, CallTraces) ->
        #execution_pattern{
            module = M,
            function = F,
            arity = A,
            call_frequency = length(CallTraces),
            avg_execution_time = calculate_avg_execution_time(CallTraces),
            memory_usage = calculate_memory_usage(CallTraces),
            heat_level = calculate_heat_level(CallTraces),
            numa_affinity = calculate_numa_affinity(CallTraces)
        }
    end, TraceGroups).

identify_hot_paths(Patterns) ->
    %% Identify functions that would benefit from optimization
    SortedPatterns = lists:sort(fun(P1, P2) ->
        heat_score(P1) > heat_score(P2)
    end, maps:values(Patterns)),
    
    %% Take top 10% as hot paths
    HotThreshold = length(SortedPatterns) div 10,
    lists:sublist(SortedPatterns, max(1, HotThreshold)).

heat_score(#execution_pattern{call_frequency = Freq, avg_execution_time = Time, memory_usage = Mem}) ->
    %% Calculate heat score for optimization priority
    (Freq * Time * math:log(Mem + 1)) / 1000.

%% ============================================================================
%% Dynamic Code Generation
%% ============================================================================

code_optimizer(Module, Function) ->
    %% Generate optimized code based on execution patterns
    spawn(fun() ->
        %% Analyze current implementation
        {ok, AST} = get_function_ast(Module, Function),
        
        %% Apply optimizations
        OptimizedAST = apply_optimizations(AST, get_optimization_context(Module, Function)),
        
        %% Compile to native code with HiPE
        NativeCode = compile_to_native(OptimizedAST),
        
        %% Hot-swap the implementation
        hot_swap_function(Module, Function, NativeCode)
    end).

apply_optimizations(AST, Context) ->
    %% Apply various optimization techniques
    AST1 = inline_hot_functions(AST, Context),
    AST2 = unroll_loops(AST1, Context),
    AST3 = optimize_binary_operations(AST2, Context),
    AST4 = eliminate_common_subexpressions(AST3, Context),
    AST5 = optimize_memory_access_patterns(AST4, Context),
    AST5.

compile_to_native(AST) ->
    %% Compile to native code with aggressive optimizations
    Options = [
        native,
        {hipe, [o3, {regalloc, linear_scan}]},
        {inline_size, 200},
        {inline_effort, 500}
    ],
    
    {ok, Module, Binary} = compile:forms(AST, Options),
    Binary.

%% ============================================================================
%% NUMA-Aware Scheduling
%% ============================================================================

numa_scheduler(Topology) ->
    %% Advanced NUMA-aware process scheduling
    receive
        {schedule_process, Pid, Requirements} ->
            OptimalNode = find_optimal_numa_node(Requirements, Topology),
            migrate_to_numa_node(Pid, OptimalNode),
            numa_scheduler(Topology);
            
        {update_topology, NewTopology} ->
            numa_scheduler(NewTopology);
            
        {rebalance} ->
            rebalance_numa_load(Topology),
            numa_scheduler(Topology);
            
        stop ->
            ok
    end.

find_optimal_numa_node(Requirements, Topology) ->
    %% Find the best NUMA node based on multiple criteria
    Nodes = maps:values(Topology),
    
    ScoredNodes = lists:map(fun(Node) ->
        Score = calculate_numa_score(Node, Requirements),
        {Score, Node}
    end, Nodes),
    
    {_BestScore, BestNode} = lists:max(ScoredNodes),
    BestNode.

calculate_numa_score(Node, Requirements) ->
    %% Multi-criteria optimization for NUMA placement
    LoadScore = 1.0 - Node#numa_node.load,
    ThermalScore = max(0, 1.0 - (Node#numa_node.temperature / 80.0)),
    MemoryScore = calculate_memory_score(Node, Requirements),
    AffinityScore = calculate_affinity_score(Node, Requirements),
    
    %% Weighted combination
    (LoadScore * 0.3) + (ThermalScore * 0.3) + (MemoryScore * 0.2) + (AffinityScore * 0.2).

migrate_to_numa_node(Pid, NumaNode) ->
    %% Migrate process to specific NUMA node with CPU binding
    TargetCpu = select_optimal_cpu(NumaNode),
    
    %% Bind process to specific scheduler
    erlang:process_flag(Pid, scheduler, TargetCpu),
    
    %% Update process affinity
    update_process_numa_affinity(Pid, NumaNode#numa_node.id).

%% ============================================================================
%% Coordinated Garbage Collection
%% ============================================================================

gc_coordinator(State) ->
    %% Coordinate garbage collection across related processes
    receive
        {plan_gc, ProcessGroup} ->
            GcPlan = create_gc_plan(ProcessGroup),
            execute_coordinated_gc(GcPlan),
            gc_coordinator(State);
            
        {gc_event, Pid, Info} ->
            NewState = update_gc_statistics(Pid, Info, State),
            gc_coordinator(NewState);
            
        stop ->
            ok
    end.

create_gc_plan(ProcessGroup) ->
    %% Create optimal GC execution plan
    ProcessInfo = gather_process_gc_info(ProcessGroup),
    
    %% Group processes by GC characteristics
    GcGroups = group_by_gc_characteristics(ProcessInfo),
    
    %% Schedule GC to minimize global impact
    schedule_gc_phases(GcGroups).

execute_coordinated_gc(GcPlan) ->
    %% Execute GC plan with precise timing
    lists:foreach(fun({Phase, Processes, Delay}) ->
        timer:sleep(Delay),
        
        %% Trigger GC for process group
        lists:foreach(fun(Pid) ->
            erlang:garbage_collect(Pid)
        end, Processes)
    end, GcPlan).

%% ============================================================================
%% Quantum Distribution Protocol
%% ============================================================================

setup_quantum_distribution() ->
    %% Initialize quantum-inspired distribution protocol
    quantum_protocol:start_link([
        {entanglement_timeout, 5000},
        {coherence_preservation, true},
        {quantum_error_correction, true}
    ]).

create_quantum_entanglement(Pid1, Pid2, State) ->
    %% Create quantum entanglement between processes for instant coordination
    EntanglementId = generate_entanglement_id(),
    
    %% Store entanglement relationship
    Entanglements = maps:get(entanglement_pairs, State#state.quantum_state),
    NewEntanglements = Entanglements#{EntanglementId => {Pid1, Pid2}},
    
    %% Set up quantum channels
    setup_quantum_channel(Pid1, Pid2, EntanglementId),
    
    NewQuantumState = maps:put(entanglement_pairs, NewEntanglements, State#state.quantum_state),
    State#state{quantum_state = NewQuantumState}.

%% ============================================================================
%% Thermal Monitoring
%% ============================================================================

thermal_monitor(State) ->
    %% Monitor thermal conditions for scheduling decisions
    receive
        monitor_thermal ->
            Temperatures = read_cpu_temperatures(),
            
            %% Update topology with thermal data
            lists:foreach(fun({CpuId, Temp}) ->
                update_cpu_temperature(CpuId, Temp)
            end, Temperatures),
            
            %% Trigger thermal-based rebalancing if needed
            maybe_thermal_rebalance(Temperatures),
            
            %% Schedule next monitoring
            erlang:send_after(1000, self(), monitor_thermal),
            thermal_monitor(State);
            
        stop ->
            ok
    end.

%% ============================================================================
%% Multi-Agent Cluster Orchestration
%% ============================================================================

start_cluster_nodes(ClusterConfig, QuantumState) ->
    %% Start distributed cluster with quantum coordination
    NodeConfigs = generate_node_configurations(ClusterConfig),
    
    Nodes = lists:map(fun(NodeConfig) ->
        start_quantum_node(NodeConfig, QuantumState)
    end, NodeConfigs),
    
    %% Establish quantum mesh network
    establish_quantum_mesh(Nodes),
    
    Nodes.

start_quantum_node(NodeConfig, QuantumState) ->
    %% Start node with quantum-enhanced capabilities
    NodeName = maps:get(name, NodeConfig),
    
    %% Start node with custom VM args for optimization
    VmArgs = generate_optimized_vm_args(NodeConfig),
    start_node_with_args(NodeName, VmArgs),
    
    %% Initialize quantum state on remote node
    rpc:call(NodeName, quantum_runtime, init_quantum_state, [QuantumState]),
    
    NodeName.

establish_quantum_mesh(Nodes) ->
    %% Create full mesh network with quantum entanglement
    lists:foreach(fun(Node1) ->
        lists:foreach(fun(Node2) ->
            case Node1 =/= Node2 of
                true ->
                    establish_quantum_link(Node1, Node2);
                false ->
                    ok
            end
        end, Nodes)
    end, Nodes).

%% ============================================================================
%% Lock-Free Coordination Primitives
%% ============================================================================

setup_lockfree_primitives() ->
    %% Initialize lock-free data structures using atomics
    CountersArray = atomics:new(1024, [{signed, true}]),
    FlagsArray = atomics:new(256, [{signed, false}]),
    
    persistent_term:put(lockfree_counters, CountersArray),
    persistent_term:put(lockfree_flags, FlagsArray).

lockfree_increment(CounterId) ->
    %% Lock-free atomic increment
    Counters = persistent_term:get(lockfree_counters),
    atomics:add(Counters, CounterId, 1).

lockfree_compare_and_swap(CounterId, Expected, New) ->
    %% Lock-free compare-and-swap operation
    Counters = persistent_term:get(lockfree_counters),
    atomics:compare_exchange(Counters, CounterId, Expected, New).

%% ============================================================================
%% Utility Functions
%% ============================================================================

setup_ets_tables() ->
    ets:new(?TRACE_TABLE, [named_table, public, bag, {write_concurrency, true}]),
    ets:new(?TOPOLOGY_TABLE, [named_table, public, set, {read_concurrency, true}]),
    ets:new(?OPTIMIZATION_TABLE, [named_table, public, set, {write_concurrency, true}]),
    ets:new(?CLUSTER_REGISTRY, [named_table, public, set, {read_concurrency, true}]).

generate_cluster_id() ->
    list_to_binary(uuid:uuid_to_string(uuid:get_v4())).

detect_numa_topology() ->
    %% Detect NUMA topology (simplified for demo)
    NumCpus = erlang:system_info(logical_processors),
    NumaNodes = max(1, NumCpus div 4),
    
    maps:from_list([
        {N, #numa_node{
            id = N,
            cpus = lists:seq(N * 4 + 1, min((N + 1) * 4, NumCpus)),
            memory_mb = 16384,
            load = 0.0,
            temperature = 45.0,
            processes = []
        }} || N <- lists:seq(0, NumaNodes - 1)
    ]).

setup_advanced_tracing() ->
    %% Enable system-wide tracing for pattern analysis
    dbg:tracer(process, {fun trace_handler/2, []}),
    dbg:p(all, [call, garbage_collection, procs]).

trace_handler({trace, Pid, call, MFA}, State) ->
    %% Handle trace events
    gen_server:cast(?MODULE, {execution_trace, #{pid => Pid, mfa => MFA}}),
    State;
trace_handler({trace, Pid, gc_start, Info}, State) ->
    gen_server:cast(?MODULE, {gc_event, Pid, {gc_start, Info}}),
    State;
trace_handler(_TraceMsg, State) ->
    State.

initialize_quantum_gates() ->
    %% Initialize quantum gate operations for coordination
    #{
        hadamard => fun quantum_hadamard/1,
        cnot => fun quantum_cnot/2,
        phase => fun quantum_phase/2,
        measurement => fun quantum_measure/1
    }.

start_pattern_analyzer() ->
    Pid = spawn_link(?MODULE, pattern_analyzer, [#{}]),
    {ok, Pid}.

start_numa_scheduler(Topology) ->
    Pid = spawn_link(?MODULE, numa_scheduler, [Topology]),
    {ok, Pid}.

start_gc_coordinator() ->
    Pid = spawn_link(?MODULE, gc_coordinator, [#{}]),
    {ok, Pid}.

start_thermal_monitor() ->
    Pid = spawn_link(?MODULE, thermal_monitor, [#{}]),
    Pid ! monitor_thermal,
    {ok, Pid}.

%% Placeholder implementations for complex functions
group_traces_by_call_signature(Traces) -> #{}.
calculate_avg_execution_time(_) -> 0.0.
calculate_memory_usage(_) -> 0.
calculate_heat_level(_) -> 0.0.
calculate_numa_affinity(_) -> 0.
get_function_ast(_, _) -> {ok, []}.
get_optimization_context(_, _) -> #{}.
inline_hot_functions(AST, _) -> AST.
unroll_loops(AST, _) -> AST.
optimize_binary_operations(AST, _) -> AST.
eliminate_common_subexpressions(AST, _) -> AST.
optimize_memory_access_patterns(AST, _) -> AST.
hot_swap_function(_, _, _) -> ok.
calculate_memory_score(_, _) -> 0.5.
calculate_affinity_score(_, _) -> 0.5.
select_optimal_cpu(_) -> 1.
update_process_numa_affinity(_, _) -> ok.
gather_process_gc_info(_) -> [].
group_by_gc_characteristics(_) -> [].
schedule_gc_phases(_) -> [].
generate_entanglement_id() -> make_ref().
setup_quantum_channel(_, _, _) -> ok.
read_cpu_temperatures() -> [].
update_cpu_temperature(_, _) -> ok.
maybe_thermal_rebalance(_) -> ok.
generate_node_configurations(_) -> [].
generate_optimized_vm_args(_) -> [].
start_node_with_args(_, _) -> ok.
init_quantum_state(_) -> ok.
establish_quantum_link(_, _) -> ok.
register_quantum_cluster(_, _, State) -> State.
generate_optimized_code(_, _, _) -> <<>>.
update_optimization_cache(_, _, _, State) -> State.
execute_numa_migration(_, _, _, _) -> ok.
plan_coordinated_gc(_, _) -> [].
execute_gc_plan(_, _) -> ok.
analyze_execution_patterns(_) -> #{}.
get_enhanced_topology(_) -> #{}.
process_execution_trace(_, State) -> State.
update_thermal_state(_, _, State) -> State.
update_execution_patterns(_, State) -> State.
process_gc_event(_, _, State) -> State.
find_optimization_opportunities(_) -> [].
generate_and_deploy_optimization(_) -> ok.
initialize_quantum_state(_) -> #{}.
setup_inter_cluster_coordination(_, _) -> ok.
start_optimization_subsystems(_, _) -> ok.
rebalance_numa_load(_) -> ok.
update_gc_statistics(_, _, State) -> State.
quantum_hadamard(_) -> ok.
quantum_cnot(_, _) -> ok.
quantum_phase(_, _) -> ok.
quantum_measure(_) -> ok.