Concurrency & Parallelism
Master concurrent programming patterns and parallel processing for efficient multi-threaded applications
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⚡ Concurrency Performance Calculator
📊 Performance Metrics
Serial Time: 10,000 ms
Parallel Time: 2508.4 ms
Actual Speedup: 3.99x
Amdahl Speedup: 3.48x
Efficiency: 99.7%
Throughput: 398.661 tasks/sec
Memory Overhead: 8.0 MB
🚨 Risk Analysis
Deadlock Risk: Medium
✅ Good parallelization - approaching theoretical maximum
Concurrency vs Parallelism
🔄 Concurrency
Dealing with lots of things at once. Tasks may be interleaved on single or multiple cores.
Characteristics:
- Task switching and scheduling
- Shared state management
- Synchronization primitives
- Can work on single core
Examples:
- Web server handling requests
- UI responsiveness
- Producer-consumer systems
⚡ Parallelism
Actually doing multiple things simultaneously. Requires multiple cores or processors.
Characteristics:
- Simultaneous execution
- Data partitioning
- Independent computations
- Requires multiple cores
Examples:
- Matrix multiplication
- Image processing
- MapReduce operations
Concurrency Models
🧵 Thread-Based
OS-managed threads with shared memory space
Pros:
- True parallelism
- Efficient for CPU-intensive tasks
- Direct OS scheduling
Cons:
- High memory overhead
- Context switching cost
- Complex synchronization
📨 Event-Driven
Single-threaded with event loop handling I/O operations
Pros:
- Low memory footprint
- No synchronization issues
- Excellent for I/O-heavy tasks
Cons:
- CPU-bound tasks block event loop
- Complex error handling
- Callback complexity
🎭 Actor Model
Isolated actors communicating through message passing
Pros:
- No shared state
- Natural fault isolation
- Distributed by design
Cons:
- Message passing overhead
- Complex state management
- Debugging complexity
📡 CSP (Go-style)
Goroutines communicating through channels
Pros:
- Lightweight goroutines
- Channel-based communication
- Easy to reason about
Cons:
- Channel deadlocks
- Memory leaks possible
- GC pressure with many goroutines
Synchronization Primitives
🔒 Mutex (Mutual Exclusion)
Purpose: Protect critical sections
Behavior: Only one thread can hold lock
Use When: Exclusive access needed
Cost: Medium overhead
📖 Read-Write Lock
Purpose: Multiple readers, exclusive writers
Behavior: Many readers OR one writer
Use When: Read-heavy workloads
Cost: Higher overhead than mutex
🎫 Semaphore
Purpose: Control access to resource pool
Behavior: N threads can proceed
Use When: Limiting concurrent access
Cost: Low overhead
🎯 Atomic Operations
Compare-And-Swap (CAS):
Atomically compare and update values
Fetch-And-Add:
Atomically increment counters
Memory Barriers:
Control instruction reordering
✅ Lock-free, highest performance
🔄 Condition Variables
Purpose:
Wait for specific conditions
Operations:
wait(), signal(), broadcast()
Use Cases:
Producer-consumer, event notifications
⚠️ Must be used with mutex
Common Concurrency Problems
💀 Deadlock
Two or more threads waiting indefinitely for each other
Four Conditions:
- Mutual exclusion
- Hold and wait
- No preemption
- Circular wait
Prevention:
- Lock ordering
- Timeouts
- Deadlock detection
🏁 Race Conditions
Outcome depends on unpredictable timing of thread execution
Common Scenarios:
- Unsynchronized shared variables
- Check-then-act operations
- Lazy initialization
Solutions:
- Proper synchronization
- Atomic operations
- Immutable data structures
🍯 Livelock
Threads actively try to resolve conflicts but make no progress
Example:
Two people trying to pass each other in a hallway, both stepping the same direction repeatedly
Solutions:
- Random backoff
- Priority-based resolution
- Coordinator thread
⭐ Starvation
Thread is perpetually denied access to shared resources
Causes:
- Unfair scheduling
- Priority inversion
- Greedy resource allocation
Solutions:
- Fair scheduling
- Priority aging
- Resource quotas
Performance Laws & Principles
📐 Amdahl's Law
Speedup = 1 / (S + P/N)
Where:
- S = Serial portion (cannot be parallelized)
- P = Parallel portion
- N = Number of processors
Key Insight:
Even small serial portions severely limit speedup
🌐 Gustafson's Law
Speedup = S + N × P
Key Difference:
Assumes problem size scales with number of processors
More Optimistic:
Better reflects real-world scenarios where we solve bigger problems with more resources
Concurrency Best Practices
✅ Do's
- • Use immutable data structures when possible
- • Prefer message passing over shared memory
- • Keep critical sections small
- • Use thread-safe collections
- • Design for failure and recovery
- • Profile and measure performance
- • Use appropriate synchronization primitives
- • Consider lock-free algorithms
❌ Don'ts
- • Don't assume operations are atomic
- • Don't use Thread.stop() or similar
- • Don't ignore race conditions
- • Don't create threads without bounds
- • Don't use shared mutable state unnecessarily
- • Don't hold locks longer than needed
- • Don't ignore deadlock possibilities
- • Don't optimize prematurely
Real-World Applications
🌐 Web Servers
Challenge: Handle thousands of concurrent connections
Solutions:
- Thread pools (Apache HTTP Server)
- Event-driven (Node.js, nginx)
- Actor model (Erlang/OTP)
- Async/await (Python asyncio)
💾 Database Systems
Challenge: ACID properties with concurrent transactions
Solutions:
- MVCC (Multi-Version Concurrency Control)
- Two-phase locking
- Optimistic concurrency control
- Lock-free data structures
🎮 Game Engines
Challenge: Real-time performance with multiple systems
Solutions:
- Job systems with work stealing
- Data-oriented design
- Lock-free algorithms
- Thread-per-system architecture
📊 Data Processing
Challenge: Process massive datasets efficiently
Solutions:
- Fork-join parallelism
- Map-reduce paradigm
- Stream processing
- SIMD optimizations
📝 Concurrency & Parallelism Quiz
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