Parallel Hash Tables are the concurrent data structures that enable multiple threads to perform insert, lookup, and delete operations simultaneously on a shared key-value store — where the design must balance throughput (millions of operations per second), correctness (linearizable or sequentially consistent behavior), and scalability (performance improving with core count rather than degrading due to contention).

Why Parallel Hash Tables Are Hard

A sequential hash table is trivial: hash the key, index into the bucket array, handle collisions. But when multiple threads operate concurrently, every access to a bucket is a potential data race. Naive locking (one mutex per table) serializes all operations. Fine-grained locking (per-bucket) improves concurrency but adds overhead and complexity. Lock-free designs eliminate locks entirely but require careful atomic operations and memory ordering.

Concurrent Hash Table Designs

• Striped Locking: Partition buckets into N stripes, each protected by an independent lock. Operations on different stripes proceed in parallel. Typical stripe count: 16-256. Java's ConcurrentHashMap (pre-Java 8) used this approach.

• Lock-Free with CAS: Open-addressing (linear probing or Robin Hood hashing) with atomic compare-and-swap for insertion. Each slot stores a key-value pair atomically (128-bit CAS for 64-bit key + 64-bit value). Lookup: probe sequentially from hash position, compare keys atomically. Insert: CAS empty slot with new key-value. No locks, no deadlocks.

• Read-Copy-Update (RCU): Optimized for read-heavy workloads. Readers access the hash table without any synchronization (no locks, no atomics). Writers create a modified copy of the affected bucket and atomically swap the pointer. Old version is garbage-collected after all readers have finished (grace period). Used in the Linux kernel.

• Cuckoo Hashing: Each key has two possible positions (from two hash functions). Insertion displaces existing keys to their alternative position (cuckoo eviction). Concurrent cuckoo hashing uses fine-grained locks on buckets with hazard-pointer-based garbage collection. Provides O(1) worst-case lookup.

GPU Hash Tables

GPU hash tables exploit massive parallelism but face unique constraints:

• Open Addressing Only: Pointer-chasing (chained hashing) causes severe warp divergence and poor memory coalescing. Linear probing with power-of-2 table sizes enables coalesced memory access.
• Atomic Insert: atomicCAS on 64-bit or 128-bit slots. GPU atomics on global memory have high latency (~100 cycles) but throughput scales with the number of warps.
• Warp-Cooperative Probing: A full warp cooperatively probes the table — each thread checks a different slot, and warp-level ballot/vote determines if the key was found. 32x improvement in probe throughput.

Performance Characteristics

Parallel Hash Tables are the essential concurrent building block — providing O(1) expected-time key-value access for multi-threaded and GPU-accelerated applications where sequential hash tables would become a serialization bottleneck.