%% 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.