Kernelized Vector Storage
Pointer Compression Techniques
Two novel techniques — Tiny Pointers (offset-based) and Kernelized Squashing (kernel function-based) — that compress pointer representations to achieve up to 60% memory savings with O(1) operations and improved cache performance.
How It Works
Two complementary techniques for memory-efficient pointer representation, each designed for different memory layout scenarios.
Memory Compression
Reduces pointer storage by storing offsets relative to a base address instead of full 64-bit pointers, achieving up to 50% memory savings.
O(1) Operations
Both compression and decompression execute in constant time using simple arithmetic, making Tiny Pointers suitable for performance-critical paths.
Cache Efficiency
Smaller data structures mean 2× more pointers per cache line, improving L1/L2/L3 hit rates by 3-5 percentage points across workloads.
Kernelized Squashing
Mathematical kernel functions (Linear, Polynomial, RBF, Sigmoid) compress pointers into compact hash table identifiers for flexible layouts.
Thread Safe
Designed for multi-threaded environments with thread-local base addresses and safe concurrent compression/decompression operations.
Energy Efficient
Reduced memory bandwidth translates to approximately 12% lower energy consumption in memory-intensive workloads.
Tiny Pointers Benchmarks
Performance comparison between regular pointers and Tiny Pointers across common workloads.
Per 1 million pointers — 50% savings with squashed IDs, ~44% including table
Kernelized Squashing Performance
Kernel functions transform pointer addresses into compact identifiers. Compare Linear, Polynomial, RBF, and Sigmoid kernels across key metrics.
Kernel Function Timing
Technique Memory Comparison
Break-even Analysis
Kernel Squashing achieves net memory savings when pointer density exceeds ~10,000 pointers. At lower densities, hash table overhead outweighs compression gains. With reverse mapping optimization, decompression achieves O(1) at the cost of additional table storage.
Read the Paper
Dive into the complete research paper with full algorithm descriptions, complexity analysis, benchmark methodology, and references.
View Full Paper