Operating SystemsBroadcast

Broadcast: Waking Multiple Waiting Threads

LevelIntermediate

Duration90 mins

TopicBroadcast

4 / 5

Performance Considerations: Analyzing Broadcast Costs

Beyond Correctness: Performance Matters

We've established that broadcast is always correct while signal requires careful analysis. But in production systems, correctness is just the starting point—performance determines whether your system can actually handle the required load.\n\nThis page takes a deep dive into the performance characteristics of broadcast, how to measure and analyze its costs, and how to make data-driven decisions about optimization. You'll learn to quantify the tradeoffs so you can make informed choices rather than guessing.

What You Will Learn

By the end of this page, you will understand how to measure broadcast performance, identify bottlenecks, compute the cost-benefit ratio of optimizations, and apply systematic performance analysis to your concurrent systems.

Building a Cost Model for Broadcast

To reason about performance, we need a model that captures the key costs involved in broadcast operations. Let's build this model from first principles.

Fundamental Cost Components:\n\n\n┌─────────────────────────────────────────────────────────────────────┐\n│ BROADCAST COST MODEL │\n├─────────────────────────────────────────────────────────────────────┤\n│ │\n│ Total Cost = C_broadcast + C_wakeups + C_mutex + C_recheck │\n│ │\n│ Where: │\n│ │\n│ C_broadcast = Cost of broadcast() call itself │\n│ = Time to iterate wait queue + kernel involvement │\n│ ≈ O(N) where N = number of waiters │\n│ │\n│ C_wakeups = Cost of N context switches (sleeping → runnable) │\n│ = N × T_context_switch │\n│ ≈ N × 2-10μs (varies by CPU, OS, load) │\n│ │\n│ C_mutex = Cost of N threads competing for mutex │\n│ = Serial acquisition time + contention overhead │\n│ ≈ N × (T_acquire + T_release) × contention_factor │\n│ │\n│ C_recheck = Cost of N threads checking their condition │\n│ = N × T_predicate_evaluation │\n│ │\n│ Useful Work = W × (T_acquire + T_work + T_release) │\n│ │\n│ Efficiency = Useful Work / Total Cost │\n│ │\n└─────────────────────────────────────────────────────────────────────┘\n

Typical Cost Values on Modern Hardware (x86-64, Linux)
Operation	Uncontended	Contended	Notes
Context switch	2-5 μs	5-15 μs	Higher under load
Mutex lock (uncontended)	15-30 ns	N/A	Atomic operation
Mutex lock (contended)	N/A	1-20 μs	Involves futex syscall
Condition variable wait	200-500 ns	1-5 μs	Depends on impl
Broadcast call (N waiters)	100 + 50×N ns	Varies	Linear in N
Cache line reload (L3 miss)	40-80 ns	100+ ns	Critical for contention

Contention Amplifies Costs

Under contention, every operation becomes more expensive due to cache coherency traffic, scheduler overhead, and waiting time. A 10× increase in cost under heavy contention is common. Always measure under realistic load conditions.

Cost Model Calculation Example
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// Example: Calculating broadcast overhead for a work queue
 
// Parameters
int N = 64;           // Number of worker threads waiting
int W = 1;            // Work items available (typical case)
double T_cs = 5.0;    // Context switch time (microseconds)
double T_mutex = 2.0; // Contended mutex acquire (microseconds)
double T_check = 0.1; // Condition check (microseconds)
double T_work = 100.0; // Actual work time (microseconds)
 
// Cost calculation
double C_wakeups = N * T_cs;           // 64 × 5 = 320 μs
double C_mutex = N * T_mutex;          // 64 × 2 = 128 μs  
double C_recheck = N * T_check;        // 64 × 0.1 = 6.4 μs
double total_overhead = C_wakeups + C_mutex + C_recheck;  // 454.4 μs
 
double useful_work = W * T_work;       // 1 × 100 = 100 μs
 
double efficiency = useful_work / (useful_work + total_overhead);
// efficiency = 100 / 554.4 = 18%
 
printf("Broadcast overhead: %.1f μs\n", total_overhead);
printf("Useful work: %.1f μs\n", useful_work);
printf("Efficiency: %.1f%%\n", efficiency * 100);
printf("Overhead ratio: %.1fx\n", total_overhead / useful_work);
 
// OUTPUT:
// Broadcast overhead: 454.4 μs
// Useful work: 100.0 μs
// Efficiency: 18.0%
// Overhead ratio: 4.5x
//
// This means 82% of time is wasted on the thundering herd!

Measuring Broadcast Performance

Theoretical models are useful for understanding, but real performance requires measurement. Here's how to measure broadcast behavior in your systems.

Key Metrics to Measure:\n\n1. Broadcast frequency: How often is broadcast() called?\n2. Waiter count per broadcast: How many threads are woken each time?\n3. Proceed ratio: What fraction of woken threads actually proceed?\n4. Mutex contention time: How long do threads wait for the mutex?\n5. Overall latency: End-to-end time from event to processing complete\n6. Throughput: Events processed per second under load

Performance Measurement Infrastructure
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#include <pthread.h>
#include <stdatomic.h>
#include <time.h>
#include <stdio.h>
 
// High-resolution timing
static inline uint64_t get_nanos() {
    struct timespec ts;
    clock_gettime(CLOCK_MONOTONIC, &ts);
    return ts.tv_sec * 1000000000ULL + ts.tv_nsec;
}
 
// Performance-instrumented condition variable
typedef struct {
    pthread_mutex_t mutex;
    pthread_cond_t cond;
    
    // Counters
    atomic_ulong broadcasts;
    atomic_ulong total_waiters_woken;
    atomic_ulong total_proceeded;
    atomic_ulong total_rewaited;
    
    // Timing accumulators (nanoseconds)
    atomic_ulong total_wait_time;
    atomic_ulong total_mutex_wait_time;
    
    // Current state
    atomic_int current_waiters;
} perf_cv_t;
 
void perf_cv_init(perf_cv_t *cv) {
    pthread_mutex_init(&cv->mutex, NULL);
    pthread_cond_init(&cv->cond, NULL);
    atomic_store(&cv->broadcasts, 0);
    atomic_store(&cv->total_waiters_woken, 0);
    atomic_store(&cv->total_proceeded, 0);
    atomic_store(&cv->total_rewaited, 0);
    atomic_store(&cv->total_wait_time, 0);
    atomic_store(&cv->total_mutex_wait_time, 0);
    atomic_store(&cv->current_waiters, 0);
}
 
// Enhanced wait that measures time
void perf_cv_wait(perf_cv_t *cv, bool *condition_was_false) {
    uint64_t start = get_nanos();
    
    atomic_fetch_add(&cv->current_waiters, 1);
    pthread_cond_wait(&cv->cond, &cv->mutex);
    atomic_fetch_sub(&cv->current_waiters, 1);
    
    uint64_t end = get_nanos();
    atomic_fetch_add(&cv->total_wait_time, end - start);
    
    *condition_was_false = true;  // Caller sets to false if proceeding
}
 
// Enhanced broadcast that records stats
void perf_cv_broadcast(perf_cv_t *cv) {
    int waiters = atomic_load(&cv->current_waiters);
    
    atomic_fetch_add(&cv->broadcasts, 1);
    atomic_fetch_add(&cv->total_waiters_woken, waiters);
    
    pthread_cond_broadcast(&cv->cond);
}
 
void perf_cv_record_proceed(perf_cv_t *cv) {
    atomic_fetch_add(&cv->total_proceeded, 1);
}
 
void perf_cv_record_rewait(perf_cv_t *cv) {
    atomic_fetch_add(&cv->total_rewaited, 1);
}
 
// Print statistics
void perf_cv_report(perf_cv_t *cv) {
    unsigned long broadcasts = atomic_load(&cv->broadcasts);
    unsigned long woken = atomic_load(&cv->total_waiters_woken);
    unsigned long proceeded = atomic_load(&cv->total_proceeded);
    unsigned long rewaited = atomic_load(&cv->total_rewaited);
    unsigned long wait_time = atomic_load(&cv->total_wait_time);
    
    printf("\n=== Condition Variable Performance Report ===\n");
    printf("Total broadcasts: %lu\n", broadcasts);
    printf("Total threads woken: %lu\n", woken);
    printf("Threads that proceeded: %lu\n", proceeded);
    printf("Threads that re-waited: %lu\n", rewaited);
    
    if (broadcasts > 0) {
        printf("\nPer-broadcast metrics:\n");
        printf("  Avg waiters per broadcast: %.2f\n", 
               (double)woken / broadcasts);
        printf("  Proceed ratio: %.1f%%\n", 
               woken > 0 ? 100.0 * proceeded / woken : 0);
        printf("  Waste ratio: %.1f%%\n", 
               woken > 0 ? 100.0 * rewaited / woken : 0);
    }
    
    if (woken > 0) {
        printf("\nTiming:\n");
        printf("  Total wait time: %.3f ms\n", wait_time / 1e6);
        printf("  Avg wait per thread: %.3f ms\n", 
               (wait_time / 1e6) / woken);
    }
    printf("=============================================\n");
}

Using perf and eBPF for System-Level Measurement:

System-Level Profiling Scripts
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#!/bin/bash
# System-level broadcast performance analysis
 
# 1. Record futex activity (underlying syscall for condvars)
echo "Recording futex activity for 60 seconds..."
sudo perf record -e 'syscalls:sys_enter_futex' \
    -e 'syscalls:sys_exit_futex' \
    -p $(pgrep your_application) -- sleep 60
 
perf report --stdio
 
# 2. eBPF script to measure broadcast behavior
cat > broadcast_latency.bt << 'EOF'
#!/usr/bin/env bpftrace
 
// Track pthread_cond_broadcast latency
uprobe:/lib/x86_64-linux-gnu/libpthread.so.0:pthread_cond_broadcast {
    @start[tid] = nsecs;
}
 
uretprobe:/lib/x86_64-linux-gnu/libpthread.so.0:pthread_cond_broadcast /@start[tid]/ {
    $latency = nsecs - @start[tid];
    @broadcast_latency_us = hist($latency / 1000);
    delete(@start[tid]);
}
 
// Count broadcasts by stack trace (to identify callers)
uprobe:/lib/x86_64-linux-gnu/libpthread.so.0:pthread_cond_broadcast {
    @broadcast_callers[ustack(3)] = count();
}
 
interval:s:30 {
    print(@broadcast_latency_us);
    print(@broadcast_callers);
    clear(@broadcast_latency_us);
    clear(@broadcast_callers);
}
EOF
 
echo "Running eBPF broadcast analysis..."
sudo bpftrace broadcast_latency.bt
 
# 3. Check context switch rates
echo "\nContext switch monitoring:"
vmstat 1 10 | awk 'NR==1 || NR==2 || NR>2 {print $1, $12, $13, $14, $15, $16}'
 
# 4. Check cache behavior (cache-line bouncing indicates contention)
echo "\nCache statistics:"
sudo perf stat -e cache-references,cache-misses,L1-dcache-load-misses \
    -p $(pgrep your_application) -- sleep 10

Signal vs Broadcast: Benchmark Analysis

Let's examine controlled benchmarks comparing signal and broadcast performance across different scenarios.

Signal vs Broadcast Benchmark
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// Benchmark: Signal vs Broadcast for work queue
 
#include <pthread.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <unistd.h>
 
#define NUM_WORKERS 64
#define NUM_WORK_ITEMS 100000
 
typedef struct {
    pthread_mutex_t mutex;
    pthread_cond_t cond;
    int work_count;
    int processed;
    int done;
} work_queue_t;
 
work_queue_t wq;
 
void *worker_thread(void *arg) {
    while (1) {
        pthread_mutex_lock(&wq.mutex);
        
        while (wq.work_count == 0 && !wq.done) {
            pthread_cond_wait(&wq.cond, &wq.mutex);
        }
        
        if (wq.done && wq.work_count == 0) {
            pthread_mutex_unlock(&wq.mutex);
            return NULL;
        }
        
        wq.work_count--;
        wq.processed++;
        
        pthread_mutex_unlock(&wq.mutex);
        
        // Simulate small amount of work
        for (volatile int i = 0; i < 1000; i++);
    }
}
 
void benchmark_broadcast() {
    wq.work_count = 0;
    wq.processed = 0;
    wq.done = 0;
    
    pthread_t workers[NUM_WORKERS];
    for (int i = 0; i < NUM_WORKERS; i++) {
        pthread_create(&workers[i], NULL, worker_thread, NULL);
    }
    
    // Give workers time to start waiting
    usleep(10000);
    
    struct timespec start, end;
    clock_gettime(CLOCK_MONOTONIC, &start);
    
    // Submit work with BROADCAST
    for (int i = 0; i < NUM_WORK_ITEMS; i++) {
        pthread_mutex_lock(&wq.mutex);
        wq.work_count++;
        pthread_cond_broadcast(&wq.cond);  // BROADCAST
        pthread_mutex_unlock(&wq.mutex);
    }
    
    // Wait for all work to complete
    while (1) {
        pthread_mutex_lock(&wq.mutex);
        if (wq.processed >= NUM_WORK_ITEMS) {
            wq.done = 1;
            pthread_cond_broadcast(&wq.cond);  // Wake for shutdown
            pthread_mutex_unlock(&wq.mutex);
            break;
        }
        pthread_mutex_unlock(&wq.mutex);
        usleep(1000);
    }
    
    clock_gettime(CLOCK_MONOTONIC, &end);
    
    for (int i = 0; i < NUM_WORKERS; i++) {
        pthread_join(workers[i], NULL);
    }
    
    double elapsed = (end.tv_sec - start.tv_sec) + 
                     (end.tv_nsec - start.tv_nsec) / 1e9;
    printf("BROADCAST: %d items in %.3f s = %.0f items/sec\n",
           NUM_WORK_ITEMS, elapsed, NUM_WORK_ITEMS / elapsed);
}
 
void benchmark_signal() {
    wq.work_count = 0;
    wq.processed = 0;
    wq.done = 0;
    
    pthread_t workers[NUM_WORKERS];
    for (int i = 0; i < NUM_WORKERS; i++) {
        pthread_create(&workers[i], NULL, worker_thread, NULL);
    }
    
    usleep(10000);
    
    struct timespec start, end;
    clock_gettime(CLOCK_MONOTONIC, &start);
    
    // Submit work with SIGNAL
    for (int i = 0; i < NUM_WORK_ITEMS; i++) {
        pthread_mutex_lock(&wq.mutex);
        wq.work_count++;
        pthread_cond_signal(&wq.cond);  // SIGNAL
        pthread_mutex_unlock(&wq.mutex);
    }
    
    while (1) {
        pthread_mutex_lock(&wq.mutex);
        if (wq.processed >= NUM_WORK_ITEMS) {
            wq.done = 1;
            pthread_cond_broadcast(&wq.cond);
            pthread_mutex_unlock(&wq.mutex);
            break;
        }
        pthread_mutex_unlock(&wq.mutex);
        usleep(1000);
    }
    
    clock_gettime(CLOCK_MONOTONIC, &end);
    
    for (int i = 0; i < NUM_WORKERS; i++) {
        pthread_join(workers[i], NULL);
    }
    
    double elapsed = (end.tv_sec - start.tv_sec) + 
                     (end.tv_nsec - start.tv_nsec) / 1e9;
    printf("SIGNAL:    %d items in %.3f s = %.0f items/sec\n",
           NUM_WORK_ITEMS, elapsed, NUM_WORK_ITEMS / elapsed);
}
 
int main() {
    pthread_mutex_init(&wq.mutex, NULL);
    pthread_cond_init(&wq.cond, NULL);
    
    printf("Workers: %d, Work items: %d\n\n", NUM_WORKERS, NUM_WORK_ITEMS);
    
    benchmark_signal();
    benchmark_broadcast();
    
    return 0;
}
 
// TYPICAL OUTPUT on 16-core machine:
// Workers: 64, Work items: 100000
//
// SIGNAL:    100000 items in 0.893 s = 112000 items/sec
// BROADCAST: 100000 items in 3.241 s = 30856 items/sec
//
// RESULT: Signal is ~3.6x faster for this workload!

Benchmark Results: Signal vs Broadcast (100K work items, varying workers)
Workers	Signal (items/sec)	Broadcast (items/sec)	Signal Speedup
4	145,000	128,000	1.1x
8	138,000	102,000	1.4x
16	130,000	72,000	1.8x
32	118,000	45,000	2.6x
64	112,000	31,000	3.6x
128	98,000	18,000	5.4x

Scaling Observation

The performance gap between signal and broadcast WIDENS as worker count increases. With 128 workers, signal is 5.4x faster. This is because the thundering herd overhead is O(N) where N is the worker count.

Latency vs Throughput Tradeoffs

Broadcast can affect latency and throughput differently. Understanding these tradeoffs helps you make the right choice for your workload.

Latency Impact

•Increased tail latency — Some requests wait for thundering herd to settle
•Mutex contention adds wait time — Threads queue for mutex access
•Cache pollution — Waking threads evict useful cache lines
•Variable latency — Depends on how many threads were waiting
•P99 affected more than P50 — Worst case is much worse than average

Throughput Impact

•Reduced overall throughput — CPU wasted on context switches
•Scalability ceiling — More workers doesn't help, may hurt
•Scheduler overhead — Managing many runnable threads is expensive
•Memory bandwidth saturation — Cache coherency traffic
•Lock convoy effects — Serialization at the mutex

Latency Distribution Example:\n\nConsider a work queue processing with 32 workers, comparing latency percentiles:\n\n\n┌────────────────────────────────────────────────────────────────┐\n│ LATENCY DISTRIBUTION COMPARISON │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ Percentile │ Signal │ Broadcast │ Difference │\n│ ─────────────────────────────────────────────────────────────│\n│ P50 (median) │ 0.8 ms │ 1.2 ms │ +50% │\n│ P90 │ 2.1 ms │ 4.8 ms │ +129% │\n│ P99 │ 5.2 ms │ 18.4 ms │ +254% │\n│ P99.9 │ 12.8 ms │ 45.2 ms │ +253% │\n│ Max │ 28.0 ms │ 112.0 ms │ +300% │\n│ │\n│ Observations: │\n│ - Median is only 50% worse (acceptable for many apps) │\n│ - P99 is 3.5x worse (significant for latency-sensitive) │\n│ - Max is 4x worse (can cause timeout issues) │\n│ │\n└────────────────────────────────────────────────────────────────┘\n

Tail Latency Matters

In distributed systems, tail latency (P99, P99.9) often matters more than median. If your service is called 100 times per user request, the P99 latency determines user experience. Broadcast's impact on tail latency can be a deal-breaker for latency-sensitive systems.

Memory and Cache Effects

Broadcast has significant effects on CPU cache behavior, which can dominate performance in high-frequency scenarios.

Cache Line Bouncing Problem:\n\nWhen multiple cores wake up and compete for the same mutex, the cache line containing the mutex bounces between cores:\n\n\n┌────────────────────────────────────────────────────────────────┐\n│ CACHE LINE BOUNCING │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ Broadcast wakes threads on CPUs 0, 1, 2, 3, 4, 5, 6, 7 │\n│ │\n│ Time │ Mutex cache line location │ Operation │\n│ ────────┼───────────────────────────┼─────────────────────── │\n│ t1 │ Core 0 L1 cache │ T0 tries acquire │\n│ t2 │ Core 1 L1 cache │ T1 tries acquire │\n│ │ (invalidate Core 0) │ │\n│ t3 │ Core 2 L1 cache │ T2 tries acquire │\n│ │ (invalidate Core 1) │ │\n│ t4 │ Core 0 L1 cache │ T0 tries AGAIN │\n│ │ (invalidate Core 2) │ │\n│ ... │ ... │ ... │\n│ │\n│ Each cache line transfer takes 40-100+ cycles │\n│ With 8 cores all contending, we get 8+ transfers │\n│ Total overhead: 320-800+ cycles just for cache coherency │\n│ │\n└────────────────────────────────────────────────────────────────┘\n

Memory Bandwidth Consumption:\n\nBeyond latency, cache line bouncing consumes memory bandwidth:

Measuring Cache Effects
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# Measure cache behavior during signal vs broadcast
 
# Setup: Run 64-worker benchmark with signal
./benchmark_signal &
PID=$!
 
# Measure cache statistics
echo "=== SIGNAL workload cache stats ==="
sudo perf stat -e cache-references,cache-misses,\
    L1-dcache-loads,L1-dcache-load-misses,\
    LLC-loads,LLC-load-misses -p $PID -- sleep 10
 
wait $PID
 
# Repeat with broadcast
./benchmark_broadcast &
PID=$!
 
echo "=== BROADCAST workload cache stats ==="
sudo perf stat -e cache-references,cache-misses,\
    L1-dcache-loads,L1-dcache-load-misses,\
    LLC-loads,LLC-load-misses -p $PID -- sleep 10
 
wait $PID
 
# Typical results (relative):
# Signal:    L1 miss rate ~2%, LLC miss rate ~0.1%
# Broadcast: L1 miss rate ~8%, LLC miss rate ~0.5%
# 
# The broadcast case shows 4x more L1 misses due to 
# cache line invalidations from mutex contention

NUMA Effects

On NUMA systems, the effects are even worse. If threads wake on different NUMA nodes, cache coherency traffic must cross the node interconnect, adding 2-3x latency. Consider NUMA-aware thread placement to mitigate this.

Optimization Decision Framework

With measurement data in hand, you can make informed decisions about whether to optimize and how.

The Decision Process:\n\n\n┌─────────────────────────────────────────────────────────────────────┐\n│ OPTIMIZATION DECISION FRAMEWORK │\n├─────────────────────────────────────────────────────────────────────┤\n│ │\n│ STEP 1: Measure current performance │\n│ │ - Waste ratio (rewaited / total woken) │\n│ │ - Throughput and latency under load │\n│ │ - CPU utilization vs useful work │\n│ ▼ │\n│ STEP 2: Calculate overhead │\n│ │ - If waste ratio < 20%: Probably not a problem │\n│ │ - If waste ratio > 50%: Likely impacting performance │\n│ │ - If waste ratio > 80%: Serious thundering herd │\n│ ▼ │\n│ STEP 3: Assess whether signal is safe │\n│ │ - Review all waiters: same type? same condition? │\n│ │ - Can you use separate CVs for different waiter types? │\n│ │ - Would the complexity be worth the gain? │\n│ ▼ │\n│ STEP 4: Implement and validate │\n│ │ - Make the change (signal, separate CVs, semaphore) │\n│ │ - Thorough testing for race conditions │\n│ │ - Re-measure to confirm improvement │\n│ ▼ │\n│ STEP 5: Monitor in production │\n│ - Watch for new bugs (deadlocks, starvation) │\n│ - Track performance metrics continuously │\n│ │\n└─────────────────────────────────────────────────────────────────────┘\n

Optimization ROI by Scenario
Scenario	Waste Ratio	Optimization	Expected Speedup	Risk
Work queue, 64 workers	90%	signal() instead of broadcast()	2-5x	Low
Prod/cons, shared CV	70%	Separate CVs	2-3x	Low
Resource pool, bulk return	30-50%	Semaphore	1.5-2x	Low
Heterogeneous waiters	Variable	Separate CVs	2-3x	Medium
Complex predicates	Variable	Keep broadcast, optimize predicate	1.2-1.5x	Low
Barrier synchronization	0%	No change needed	N/A	N/A

The 80% Rule

If your waste ratio is >80%, you're spending 5x more time on overhead than useful work. This is almost always worth optimizing. Below 20% waste, the optimization complexity may not be worth it unless you're in an extremely latency-sensitive path.

Advanced Optimization Techniques

Beyond the basic mitigations, here are advanced techniques for performance-critical systems.

Batch Multiple Events Before Signaling\n\nInstead of signaling for each event, batch events and signal once:

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// Batch events to reduce signal/broadcast overhead
 
typedef struct {
    pthread_mutex_t mutex;
    pthread_cond_t cond;
    queue_t *pending;
    int batch_size;
    int current_count;
} batched_queue_t;
 
// Add item without immediate signal
void queue_add_batched(batched_queue_t *q, void *item) {
    pthread_mutex_lock(&q->mutex);
    
    queue_enqueue(q->pending, item);
    q->current_count++;
    
    // Only signal when batch is full
    if (q->current_count >= q->batch_size) {
        // Signal once for entire batch
        pthread_cond_signal(&q->cond);
        q->current_count = 0;
    }
    
    pthread_mutex_unlock(&q->mutex);
}
 
// Flush remaining items (call periodically or on shutdown)
void queue_flush(batched_queue_t *q) {
    pthread_mutex_lock(&q->mutex);
    if (q->current_count > 0) {
        pthread_cond_signal(&q->cond);
        q->current_count = 0;
    }
    pthread_mutex_unlock(&q->mutex);
}
 
// Worker processes multiple items per wakeup
void *worker(void *arg) {
    batched_queue_t *q = (batched_queue_t *)arg;
    
    while (1) {
        pthread_mutex_lock(&q->mutex);
        
        while (queue_is_empty(q->pending)) {
            pthread_cond_wait(&q->cond, &q->mutex);
        }
        
        // Process ALL available items, not just one
        while (!queue_is_empty(q->pending)) {
            void *item = queue_dequeue(q->pending);
            pthread_mutex_unlock(&q->mutex);
            
            process(item);  // Outside lock
            
            pthread_mutex_lock(&q->mutex);
        }
        
        pthread_mutex_unlock(&q->mutex);
    }
}
 
// Benefits:
// - Amortizes wakeup cost across multiple items
// - Reduces context switches
// - Better cache utilization (process while data is hot)

Summary: Performance Considerations

Key Takeaways

•Build a cost model — Understand the components: context switches, mutex contention, cache effects, condition checks.
•Measure, don't guess — Instrument your code to track broadcast frequency, waiter counts, and waste ratios.
•Benchmark under realistic load — Performance characteristics change dramatically under contention.
•Signal can be 3-5x faster — For high worker counts, the difference is substantial and grows with scale.
•Tail latency is heavily impacted — P99 may be 3x worse with broadcast even when median is acceptable.
•Cache effects dominate at scale — Cache line bouncing becomes the major overhead with many cores.
•Use the 80% rule — If waste ratio exceeds 80%, optimization will likely provide significant gains.
•Advanced techniques exist — Batching, direct futex, and lock-free structures for extreme cases.

Next Steps:\n\nThe final page explores use cases—real-world scenarios and patterns where broadcast is the right choice, with complete, production-ready implementations you can adapt to your own systems.

Page Complete

You now have the tools to measure, analyze, and optimize broadcast performance in your systems. You can quantify the costs, make data-driven decisions, and apply the right optimization techniques for your workload. Next, we'll see broadcast in action with complete use case implementations.

4 / 5

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Operating SystemsBroadcast

Broadcast: Waking Multiple Waiting Threads

LevelIntermediate

Duration90 mins

TopicBroadcast

4 / 5

Performance Considerations: Analyzing Broadcast Costs

Beyond Correctness: Performance Matters

What You Will Learn

Building a Cost Model for Broadcast

To reason about performance, we need a model that captures the key costs involved in broadcast operations. Let's build this model from first principles.

Typical Cost Values on Modern Hardware (x86-64, Linux)
Operation	Uncontended	Contended	Notes
Context switch	2-5 μs	5-15 μs	Higher under load
Mutex lock (uncontended)	15-30 ns	N/A	Atomic operation
Mutex lock (contended)	N/A	1-20 μs	Involves futex syscall
Condition variable wait	200-500 ns	1-5 μs	Depends on impl
Broadcast call (N waiters)	100 + 50×N ns	Varies	Linear in N
Cache line reload (L3 miss)	40-80 ns	100+ ns	Critical for contention

Contention Amplifies Costs

Cost Model Calculation Example
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// Example: Calculating broadcast overhead for a work queue
 
// Parameters
int N = 64;           // Number of worker threads waiting
int W = 1;            // Work items available (typical case)
double T_cs = 5.0;    // Context switch time (microseconds)
double T_mutex = 2.0; // Contended mutex acquire (microseconds)
double T_check = 0.1; // Condition check (microseconds)
double T_work = 100.0; // Actual work time (microseconds)
 
// Cost calculation
double C_wakeups = N * T_cs;           // 64 × 5 = 320 μs
double C_mutex = N * T_mutex;          // 64 × 2 = 128 μs  
double C_recheck = N * T_check;        // 64 × 0.1 = 6.4 μs
double total_overhead = C_wakeups + C_mutex + C_recheck;  // 454.4 μs
 
double useful_work = W * T_work;       // 1 × 100 = 100 μs
 
double efficiency = useful_work / (useful_work + total_overhead);
// efficiency = 100 / 554.4 = 18%
 
printf("Broadcast overhead: %.1f μs\n", total_overhead);
printf("Useful work: %.1f μs\n", useful_work);
printf("Efficiency: %.1f%%\n", efficiency * 100);
printf("Overhead ratio: %.1fx\n", total_overhead / useful_work);
 
// OUTPUT:
// Broadcast overhead: 454.4 μs
// Useful work: 100.0 μs
// Efficiency: 18.0%
// Overhead ratio: 4.5x
//
// This means 82% of time is wasted on the thundering herd!

Measuring Broadcast Performance

Theoretical models are useful for understanding, but real performance requires measurement. Here's how to measure broadcast behavior in your systems.

Performance Measurement Infrastructure
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#include <pthread.h>
#include <stdatomic.h>
#include <time.h>
#include <stdio.h>
 
// High-resolution timing
static inline uint64_t get_nanos() {
    struct timespec ts;
    clock_gettime(CLOCK_MONOTONIC, &ts);
    return ts.tv_sec * 1000000000ULL + ts.tv_nsec;
}
 
// Performance-instrumented condition variable
typedef struct {
    pthread_mutex_t mutex;
    pthread_cond_t cond;
    
    // Counters
    atomic_ulong broadcasts;
    atomic_ulong total_waiters_woken;
    atomic_ulong total_proceeded;
    atomic_ulong total_rewaited;
    
    // Timing accumulators (nanoseconds)
    atomic_ulong total_wait_time;
    atomic_ulong total_mutex_wait_time;
    
    // Current state
    atomic_int current_waiters;
} perf_cv_t;
 
void perf_cv_init(perf_cv_t *cv) {
    pthread_mutex_init(&cv->mutex, NULL);
    pthread_cond_init(&cv->cond, NULL);
    atomic_store(&cv->broadcasts, 0);
    atomic_store(&cv->total_waiters_woken, 0);
    atomic_store(&cv->total_proceeded, 0);
    atomic_store(&cv->total_rewaited, 0);
    atomic_store(&cv->total_wait_time, 0);
    atomic_store(&cv->total_mutex_wait_time, 0);
    atomic_store(&cv->current_waiters, 0);
}
 
// Enhanced wait that measures time
void perf_cv_wait(perf_cv_t *cv, bool *condition_was_false) {
    uint64_t start = get_nanos();
    
    atomic_fetch_add(&cv->current_waiters, 1);
    pthread_cond_wait(&cv->cond, &cv->mutex);
    atomic_fetch_sub(&cv->current_waiters, 1);
    
    uint64_t end = get_nanos();
    atomic_fetch_add(&cv->total_wait_time, end - start);
    
    *condition_was_false = true;  // Caller sets to false if proceeding
}
 
// Enhanced broadcast that records stats
void perf_cv_broadcast(perf_cv_t *cv) {
    int waiters = atomic_load(&cv->current_waiters);
    
    atomic_fetch_add(&cv->broadcasts, 1);
    atomic_fetch_add(&cv->total_waiters_woken, waiters);
    
    pthread_cond_broadcast(&cv->cond);
}
 
void perf_cv_record_proceed(perf_cv_t *cv) {
    atomic_fetch_add(&cv->total_proceeded, 1);
}
 
void perf_cv_record_rewait(perf_cv_t *cv) {
    atomic_fetch_add(&cv->total_rewaited, 1);
}
 
// Print statistics
void perf_cv_report(perf_cv_t *cv) {
    unsigned long broadcasts = atomic_load(&cv->broadcasts);
    unsigned long woken = atomic_load(&cv->total_waiters_woken);
    unsigned long proceeded = atomic_load(&cv->total_proceeded);
    unsigned long rewaited = atomic_load(&cv->total_rewaited);
    unsigned long wait_time = atomic_load(&cv->total_wait_time);
    
    printf("\n=== Condition Variable Performance Report ===\n");
    printf("Total broadcasts: %lu\n", broadcasts);
    printf("Total threads woken: %lu\n", woken);
    printf("Threads that proceeded: %lu\n", proceeded);
    printf("Threads that re-waited: %lu\n", rewaited);
    
    if (broadcasts > 0) {
        printf("\nPer-broadcast metrics:\n");
        printf("  Avg waiters per broadcast: %.2f\n", 
               (double)woken / broadcasts);
        printf("  Proceed ratio: %.1f%%\n", 
               woken > 0 ? 100.0 * proceeded / woken : 0);
        printf("  Waste ratio: %.1f%%\n", 
               woken > 0 ? 100.0 * rewaited / woken : 0);
    }
    
    if (woken > 0) {
        printf("\nTiming:\n");
        printf("  Total wait time: %.3f ms\n", wait_time / 1e6);
        printf("  Avg wait per thread: %.3f ms\n", 
               (wait_time / 1e6) / woken);
    }
    printf("=============================================\n");
}

Using perf and eBPF for System-Level Measurement:

System-Level Profiling Scripts
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#!/bin/bash
# System-level broadcast performance analysis
 
# 1. Record futex activity (underlying syscall for condvars)
echo "Recording futex activity for 60 seconds..."
sudo perf record -e 'syscalls:sys_enter_futex' \
    -e 'syscalls:sys_exit_futex' \
    -p $(pgrep your_application) -- sleep 60
 
perf report --stdio
 
# 2. eBPF script to measure broadcast behavior
cat > broadcast_latency.bt << 'EOF'
#!/usr/bin/env bpftrace
 
// Track pthread_cond_broadcast latency
uprobe:/lib/x86_64-linux-gnu/libpthread.so.0:pthread_cond_broadcast {
    @start[tid] = nsecs;
}
 
uretprobe:/lib/x86_64-linux-gnu/libpthread.so.0:pthread_cond_broadcast /@start[tid]/ {
    $latency = nsecs - @start[tid];
    @broadcast_latency_us = hist($latency / 1000);
    delete(@start[tid]);
}
 
// Count broadcasts by stack trace (to identify callers)
uprobe:/lib/x86_64-linux-gnu/libpthread.so.0:pthread_cond_broadcast {
    @broadcast_callers[ustack(3)] = count();
}
 
interval:s:30 {
    print(@broadcast_latency_us);
    print(@broadcast_callers);
    clear(@broadcast_latency_us);
    clear(@broadcast_callers);
}
EOF
 
echo "Running eBPF broadcast analysis..."
sudo bpftrace broadcast_latency.bt
 
# 3. Check context switch rates
echo "\nContext switch monitoring:"
vmstat 1 10 | awk 'NR==1 || NR==2 || NR>2 {print $1, $12, $13, $14, $15, $16}'
 
# 4. Check cache behavior (cache-line bouncing indicates contention)
echo "\nCache statistics:"
sudo perf stat -e cache-references,cache-misses,L1-dcache-load-misses \
    -p $(pgrep your_application) -- sleep 10

Signal vs Broadcast: Benchmark Analysis

Let's examine controlled benchmarks comparing signal and broadcast performance across different scenarios.

Signal vs Broadcast Benchmark
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// Benchmark: Signal vs Broadcast for work queue
 
#include <pthread.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <unistd.h>
 
#define NUM_WORKERS 64
#define NUM_WORK_ITEMS 100000
 
typedef struct {
    pthread_mutex_t mutex;
    pthread_cond_t cond;
    int work_count;
    int processed;
    int done;
} work_queue_t;
 
work_queue_t wq;
 
void *worker_thread(void *arg) {
    while (1) {
        pthread_mutex_lock(&wq.mutex);
        
        while (wq.work_count == 0 && !wq.done) {
            pthread_cond_wait(&wq.cond, &wq.mutex);
        }
        
        if (wq.done && wq.work_count == 0) {
            pthread_mutex_unlock(&wq.mutex);
            return NULL;
        }
        
        wq.work_count--;
        wq.processed++;
        
        pthread_mutex_unlock(&wq.mutex);
        
        // Simulate small amount of work
        for (volatile int i = 0; i < 1000; i++);
    }
}
 
void benchmark_broadcast() {
    wq.work_count = 0;
    wq.processed = 0;
    wq.done = 0;
    
    pthread_t workers[NUM_WORKERS];
    for (int i = 0; i < NUM_WORKERS; i++) {
        pthread_create(&workers[i], NULL, worker_thread, NULL);
    }
    
    // Give workers time to start waiting
    usleep(10000);
    
    struct timespec start, end;
    clock_gettime(CLOCK_MONOTONIC, &start);
    
    // Submit work with BROADCAST
    for (int i = 0; i < NUM_WORK_ITEMS; i++) {
        pthread_mutex_lock(&wq.mutex);
        wq.work_count++;
        pthread_cond_broadcast(&wq.cond);  // BROADCAST
        pthread_mutex_unlock(&wq.mutex);
    }
    
    // Wait for all work to complete
    while (1) {
        pthread_mutex_lock(&wq.mutex);
        if (wq.processed >= NUM_WORK_ITEMS) {
            wq.done = 1;
            pthread_cond_broadcast(&wq.cond);  // Wake for shutdown
            pthread_mutex_unlock(&wq.mutex);
            break;
        }
        pthread_mutex_unlock(&wq.mutex);
        usleep(1000);
    }
    
    clock_gettime(CLOCK_MONOTONIC, &end);
    
    for (int i = 0; i < NUM_WORKERS; i++) {
        pthread_join(workers[i], NULL);
    }
    
    double elapsed = (end.tv_sec - start.tv_sec) + 
                     (end.tv_nsec - start.tv_nsec) / 1e9;
    printf("BROADCAST: %d items in %.3f s = %.0f items/sec\n",
           NUM_WORK_ITEMS, elapsed, NUM_WORK_ITEMS / elapsed);
}
 
void benchmark_signal() {
    wq.work_count = 0;
    wq.processed = 0;
    wq.done = 0;
    
    pthread_t workers[NUM_WORKERS];
    for (int i = 0; i < NUM_WORKERS; i++) {
        pthread_create(&workers[i], NULL, worker_thread, NULL);
    }
    
    usleep(10000);
    
    struct timespec start, end;
    clock_gettime(CLOCK_MONOTONIC, &start);
    
    // Submit work with SIGNAL
    for (int i = 0; i < NUM_WORK_ITEMS; i++) {
        pthread_mutex_lock(&wq.mutex);
        wq.work_count++;
        pthread_cond_signal(&wq.cond);  // SIGNAL
        pthread_mutex_unlock(&wq.mutex);
    }
    
    while (1) {
        pthread_mutex_lock(&wq.mutex);
        if (wq.processed >= NUM_WORK_ITEMS) {
            wq.done = 1;
            pthread_cond_broadcast(&wq.cond);
            pthread_mutex_unlock(&wq.mutex);
            break;
        }
        pthread_mutex_unlock(&wq.mutex);
        usleep(1000);
    }
    
    clock_gettime(CLOCK_MONOTONIC, &end);
    
    for (int i = 0; i < NUM_WORKERS; i++) {
        pthread_join(workers[i], NULL);
    }
    
    double elapsed = (end.tv_sec - start.tv_sec) + 
                     (end.tv_nsec - start.tv_nsec) / 1e9;
    printf("SIGNAL:    %d items in %.3f s = %.0f items/sec\n",
           NUM_WORK_ITEMS, elapsed, NUM_WORK_ITEMS / elapsed);
}
 
int main() {
    pthread_mutex_init(&wq.mutex, NULL);
    pthread_cond_init(&wq.cond, NULL);
    
    printf("Workers: %d, Work items: %d\n\n", NUM_WORKERS, NUM_WORK_ITEMS);
    
    benchmark_signal();
    benchmark_broadcast();
    
    return 0;
}
 
// TYPICAL OUTPUT on 16-core machine:
// Workers: 64, Work items: 100000
//
// SIGNAL:    100000 items in 0.893 s = 112000 items/sec
// BROADCAST: 100000 items in 3.241 s = 30856 items/sec
//
// RESULT: Signal is ~3.6x faster for this workload!

Benchmark Results: Signal vs Broadcast (100K work items, varying workers)
Workers	Signal (items/sec)	Broadcast (items/sec)	Signal Speedup
4	145,000	128,000	1.1x
8	138,000	102,000	1.4x
16	130,000	72,000	1.8x
32	118,000	45,000	2.6x
64	112,000	31,000	3.6x
128	98,000	18,000	5.4x

Scaling Observation

Latency vs Throughput Tradeoffs

Broadcast can affect latency and throughput differently. Understanding these tradeoffs helps you make the right choice for your workload.

Latency Impact

•Increased tail latency — Some requests wait for thundering herd to settle
•Mutex contention adds wait time — Threads queue for mutex access
•Cache pollution — Waking threads evict useful cache lines
•Variable latency — Depends on how many threads were waiting
•P99 affected more than P50 — Worst case is much worse than average

Throughput Impact

•Reduced overall throughput — CPU wasted on context switches
•Scalability ceiling — More workers doesn't help, may hurt
•Scheduler overhead — Managing many runnable threads is expensive
•Memory bandwidth saturation — Cache coherency traffic
•Lock convoy effects — Serialization at the mutex

Tail Latency Matters

Memory and Cache Effects

Broadcast has significant effects on CPU cache behavior, which can dominate performance in high-frequency scenarios.

Memory Bandwidth Consumption:\n\nBeyond latency, cache line bouncing consumes memory bandwidth:

Measuring Cache Effects
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# Measure cache behavior during signal vs broadcast
 
# Setup: Run 64-worker benchmark with signal
./benchmark_signal &
PID=$!
 
# Measure cache statistics
echo "=== SIGNAL workload cache stats ==="
sudo perf stat -e cache-references,cache-misses,\
    L1-dcache-loads,L1-dcache-load-misses,\
    LLC-loads,LLC-load-misses -p $PID -- sleep 10
 
wait $PID
 
# Repeat with broadcast
./benchmark_broadcast &
PID=$!
 
echo "=== BROADCAST workload cache stats ==="
sudo perf stat -e cache-references,cache-misses,\
    L1-dcache-loads,L1-dcache-load-misses,\
    LLC-loads,LLC-load-misses -p $PID -- sleep 10
 
wait $PID
 
# Typical results (relative):
# Signal:    L1 miss rate ~2%, LLC miss rate ~0.1%
# Broadcast: L1 miss rate ~8%, LLC miss rate ~0.5%
# 
# The broadcast case shows 4x more L1 misses due to 
# cache line invalidations from mutex contention

NUMA Effects

Optimization Decision Framework

With measurement data in hand, you can make informed decisions about whether to optimize and how.

Optimization ROI by Scenario
Scenario	Waste Ratio	Optimization	Expected Speedup	Risk
Work queue, 64 workers	90%	signal() instead of broadcast()	2-5x	Low
Prod/cons, shared CV	70%	Separate CVs	2-3x	Low
Resource pool, bulk return	30-50%	Semaphore	1.5-2x	Low
Heterogeneous waiters	Variable	Separate CVs	2-3x	Medium
Complex predicates	Variable	Keep broadcast, optimize predicate	1.2-1.5x	Low
Barrier synchronization	0%	No change needed	N/A	N/A

The 80% Rule

Advanced Optimization Techniques

Beyond the basic mitigations, here are advanced techniques for performance-critical systems.

Batch Multiple Events Before Signaling\n\nInstead of signaling for each event, batch events and signal once:

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// Batch events to reduce signal/broadcast overhead
 
typedef struct {
    pthread_mutex_t mutex;
    pthread_cond_t cond;
    queue_t *pending;
    int batch_size;
    int current_count;
} batched_queue_t;
 
// Add item without immediate signal
void queue_add_batched(batched_queue_t *q, void *item) {
    pthread_mutex_lock(&q->mutex);
    
    queue_enqueue(q->pending, item);
    q->current_count++;
    
    // Only signal when batch is full
    if (q->current_count >= q->batch_size) {
        // Signal once for entire batch
        pthread_cond_signal(&q->cond);
        q->current_count = 0;
    }
    
    pthread_mutex_unlock(&q->mutex);
}
 
// Flush remaining items (call periodically or on shutdown)
void queue_flush(batched_queue_t *q) {
    pthread_mutex_lock(&q->mutex);
    if (q->current_count > 0) {
        pthread_cond_signal(&q->cond);
        q->current_count = 0;
    }
    pthread_mutex_unlock(&q->mutex);
}
 
// Worker processes multiple items per wakeup
void *worker(void *arg) {
    batched_queue_t *q = (batched_queue_t *)arg;
    
    while (1) {
        pthread_mutex_lock(&q->mutex);
        
        while (queue_is_empty(q->pending)) {
            pthread_cond_wait(&q->cond, &q->mutex);
        }
        
        // Process ALL available items, not just one
        while (!queue_is_empty(q->pending)) {
            void *item = queue_dequeue(q->pending);
            pthread_mutex_unlock(&q->mutex);
            
            process(item);  // Outside lock
            
            pthread_mutex_lock(&q->mutex);
        }
        
        pthread_mutex_unlock(&q->mutex);
    }
}
 
// Benefits:
// - Amortizes wakeup cost across multiple items
// - Reduces context switches
// - Better cache utilization (process while data is hot)

Summary: Performance Considerations

Key Takeaways

•Build a cost model — Understand the components: context switches, mutex contention, cache effects, condition checks.
•Measure, don't guess — Instrument your code to track broadcast frequency, waiter counts, and waste ratios.
•Benchmark under realistic load — Performance characteristics change dramatically under contention.
•Signal can be 3-5x faster — For high worker counts, the difference is substantial and grows with scale.
•Tail latency is heavily impacted — P99 may be 3x worse with broadcast even when median is acceptable.
•Cache effects dominate at scale — Cache line bouncing becomes the major overhead with many cores.
•Use the 80% rule — If waste ratio exceeds 80%, optimization will likely provide significant gains.
•Advanced techniques exist — Batching, direct futex, and lock-free structures for extreme cases.

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