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• HashSmith provides multiple high-performance hash table implementations optimized for speed and memory efficiency on modern JVMs.
• Focus areas: SWAR-probing (SwissMap), SIMD-probing (SwissSimdMap), predictable probe lengths (Robin Hood), and minimal per-entry overhead.
• Built for JDK 21+;SwissSimdMap uses the incubating Vector API for SIMD acceleration.
• More memory-efficient than the built-in JDKHashMap; performance depends on workload.

• SwissMap: SwissTable-inspired design using SWAR control-byte probing (no Vector API) with tombstone reuse. Default map.
• SwissSimdMap: SIMD (Vector API incubator) variant of SwissMap with vectorized control-byte probing. Seedocs/SwissSimdMap.md for details.
• ConcurrentSwissMap: sharded, thread-safe wrapper aroundSwissMap using per-shardStampedLock (null keys not supported).
• SwissSet: SwissTable-style hash set with SIMD control-byte probing, tombstone reuse, and null-element support

Vector API is still incubating, and profiling on my setup showed the SIMD path taking longer than expected, so the defaultSwissMap favors a SWAR probe. Numbers can differ significantly by hardware/JVM version; please run your own benchmarks if you plan to useSwissSimdMap.

• If you want a guided tour with design notes and benchmarks, seethis write-up.

import io.github.bluuewhale.hashsmith.SwissMap;      // SWARimport io.github.bluuewhale.hashsmith.ConcurrentSwissMap;import io.github.bluuewhale.hashsmith.SwissSet;// SwissMap (SWAR)var swiss = new SwissMap<String, Integer>();swiss.put("a", 1);swiss.get("a"); // 1// ConcurrentSwissMap (sharded, thread-safe)var concurrentSwiss = new ConcurrentSwissMap<String, Integer>();concurrentSwiss.put("a", 1);concurrentSwiss.get("a"); // 1// SwissSetvar swissSet = new SwissSet<String>();swissSet.add("k");swissSet.add(null); // nulls allowedswissSet.contains("k"); // true

• Gradle (Kotlin DSL):

dependencies {    implementation("io.github.bluuewhale:hashsmith:0.1.8")}

• Gradle (Groovy):

dependencies {    implementation 'io.github.bluuewhale:hashsmith:0.1.8'}

• Maven:

<dependency>  <groupId>io.github.bluuewhale</groupId>  <artifactId>hashsmith</artifactId>  <version>0.1.8</version></dependency>

• JDK 21+ (SwissSimdMap needsjdk.incubator.vector)
• Gradle (wrapper provided)
• The JVM flag--add-modules jdk.incubator.vector is already configured for build, test, and JMH tasks that exerciseSwissSimdMap.

./gradlew build        # full build./gradlew test         # JUnit 5 tests

• Compares retained heap for both maps (HashMap vsSwissSimdMap vsSwissMap vs fastutilObject2ObjectOpenHashMap vs Eclipse CollectionsUnifiedMap) and sets (HashSet vsSwissSet vs fastutilObjectOpenHashSet vs Eclipse CollectionsUnifiedSet).
• Set benchmarks use UUIDString keys (HashSet, SwissSet, ObjectOpenHashSet, UnifiedSet). Primitive-specialized collections (e.g., fastutil primitive sets) are excluded because their memory profile is driven by primitive storage, whereas these tests target general reference workloads.

• Maps:SwissMap/SwissSimdMap use open addressing to cut space; default load factor 0.875, up to 53.3% retained-heap reduction in payload-light cases vsHashMap.
• Sets:SwissSet (SwissHashSet) mirrors the SwissTable layout with SIMD control-byte probing and reuses tombstones to stay denser thanHashSet across tested payloads, showing up to ~62% retained-heap reduction in lighter payload cases.

Map	Set

• All benchmarks were run on Windows 11 (x64) with Eclipse Temurin JDK 21.0.9, on an AMD Ryzen 5 5600 (6C/12T).
• At high load factors SwissMap (SWAR) stay competitive against other open-addressing tables and close to JDK HashMap performance; depending on hardware/JVM, one may edge out the other.

put hit	put miss

get hit	get miss

1. Open an issue for bugs/ideas
2. Work on a feature branch and open a PR
3. Keep tests/JMH green before submitting

• This project is licensed under the MIT License. SeeLICENSE for details.