Parallel Hash Table

Parallel Hash Tables are the concurrent data structures that allow multiple threads to simultaneously insert, lookup, and delete key-value pairs with high throughput and low contention — requiring careful design of hash functions, collision resolution, and synchronization mechanisms to avoid the serialization bottleneck of lock-based approaches, with implementations spanning CPU lock-free designs, GPU-optimized cuckoo hashing, and distributed hash tables that scale to billions of entries across multiple machines.

Why Parallel Hash Tables Are Hard

• Sequential hash table: O(1) average insert/lookup → extremely efficient.
• Naive parallel: Lock the entire table → only one thread operates at a time → no speedup.
• Fine-grained locking: Lock per bucket → better but lock overhead + contention on hot buckets.
• Lock-free: Use atomic CAS (Compare-and-Swap) → best throughput but complex implementation.

Concurrent Hash Table Approaches

Lock-Free Insert (Linear Probing)

bool insert(HashTable *ht, uint64_t key, uint64_t value) {
    uint64_t slot = hash(key) % ht->capacity;
    while (true) {
        uint64_t existing = __atomic_load(&ht->keys[slot]);
        if (existing == EMPTY) {
            // CAS: atomically try to claim slot
            if (__atomic_compare_exchange(&ht->keys[slot],
                    &existing, key)) {
                __atomic_store(&ht->vals[slot], value);
                return true;  // Inserted
            }
        }
        if (existing == key) return false;  // Duplicate
        slot = (slot + 1) % ht->capacity;   // Probe next
    }
}

GPU Hash Tables

• GPU has thousands of threads → extreme parallelism → needs specialized design.
• Challenges: No per-thread locks (too expensive), shared memory limited, warp-level coordination.
• GPU Cuckoo Hashing:
• Two hash functions h₁, h₂. Each key has exactly 2 possible locations.
• Insert: Try h₁, if occupied → evict to h₂ of displaced key → chain continues.
• Lookup: Check only 2 locations → constant time, cache-friendly.
• GPU-friendly: Fixed number of memory accesses → predictable performance.

cuDPP / SlabHash (GPU)

// Build hash table on GPU (millions of inserts in parallel)
gpu_hash_table_build(keys, values, num_entries, table);

// Parallel lookup
gpu_hash_table_lookup(query_keys, num_queries, table, results);
// Throughput: 500M+ inserts/sec on modern GPU

Distributed Hash Tables (DHT)

• Keys distributed across nodes: node = hash(key) % num_nodes.
• Each node holds a local hash table for its key range.
• Consistent hashing: Minimize redistribution when nodes join/leave.
• Examples: Redis Cluster, Memcached, Amazon DynamoDB (underlying).

Performance Benchmarks

Parallel hash tables are the fundamental building block for high-throughput concurrent data access — from database query engines to GPU-accelerated graph analytics to distributed caching systems, the ability to perform billions of key-value operations per second across many threads is essential for any system that must maintain fast random access to large datasets under heavy concurrent load.