GPU Acceleration Research for Unirust

This document analyzes GPU acceleration opportunities for the Unirust entity resolution system, drawing insights from the Sirius-DB GPU-native database project and comprehensive codebase analysis.

Executive Summary

Unirust's architecture presents several high-impact GPU acceleration opportunities, particularly in batch hashing, interval matching, and cross-shard reconciliation. Conservative estimates suggest 10-50x speedups for signature generation and 5-30x for conflict detection at scale. The DSU (union-find) operations should remain on CPU due to sequential data dependencies.

Background: GPU Database Systems
Unirust Architecture Analysis
GPU Acceleration Candidates
Implementation Recommendations
Architecture Design
Performance Projections
References

Background: GPU Database Systems

Why GPUs for Data Processing?

Modern GPUs offer massive parallelism that traditional CPUs cannot match:

Metric	CPU (AMD EPYC 9654)	GPU (NVIDIA H100)	Ratio
Cores	96	16,896 CUDA cores	176x
Memory Bandwidth	~460 GB/s	3.35 TB/s (HBM3)	7.3x
FP64 TFLOPS	~2.5	67	27x
Power	360W	700W	1.9x

Lessons from Sirius-DB

Sirius is a GPU-native SQL engine from University of Wisconsin-Madison that achieves 7-12x speedups over DuckDB/ClickHouse. Key architectural insights:

1. GPU-Native vs Hybrid Approach

"Sirius treats GPUs as the primary execution engine, aiming to run the entire query plan—from scan to result—on the GPU. It differs from systems that retrofit GPU acceleration onto traditional CPU-optimized engines."

Application to Unirust: Don't try to GPU-accelerate everything. Identify batch-parallelizable operations and keep sequential operations (DSU mutations) on CPU.

2. Memory Management Strategy

Sirius divides GPU memory into two regions:

Data Caching: Pre-allocated storage for frequently accessed data
Data Processing: RMM pool allocator for intermediate results (hash tables, temporaries)

Application to Unirust: Pre-allocate GPU memory for:

Identity key signatures (read-heavy)
Interval tree nodes (query-heavy)
Conflict detection scratch space

3. Push-Based Execution Model

Operators are stateless; data is pushed through the pipeline rather than pulled. This simplifies GPU kernel design and reduces synchronization overhead.

Application to Unirust: Stream record batches to GPU, return cluster assignments and conflict flags.

4. Workloads That Benefit Most

From Sirius benchmarks on TPC-H:

Join-heavy queries (Q2-Q5, Q7-Q8, Q20-Q22): Highest GPU benefit
Large aggregations: Memory bandwidth advantage
Scan-intensive workloads: 3TB/s HBM vs ~400GB/s DDR5

Application to Unirust: Interval matching is essentially a temporal join. Cross-shard reconciliation involves large aggregations.

Unirust Architecture Analysis

Current Compute Hotspots

Based on profiling and code analysis, the following operations dominate compute time:

1. Identity Key Signature Generation (`src/sharding.rs`)

pub fn compute_identity_key_signature(
    entity_type: &str,
    key_values: &[String],
) -> IdentityKeySignature {
    let mut sig = [0u8; 32];

    // First hasher: bytes 0-15
    let mut hasher1 = DefaultHasher::new();
    entity_type.hash(&mut hasher1);
    for value in key_values {
        value.hash(&mut hasher1);
    }
    let hash1 = hasher1.finish();
    sig[0..8].copy_from_slice(&hash1.to_le_bytes());

    // Second hasher with XOR variations: bytes 8-31
    // ...
}

Characteristics:

Called once per record during ingestion
Called during cross-shard reconciliation (potentially millions of times)
Pure computation, no dependencies between records
Ideal for GPU: Embarrassingly parallel

2. Interval Tree Queries (`src/index.rs`)

impl IntervalTree {
    pub fn collect_overlapping_limited(
        &mut self,
        interval: Interval,
        max_nodes: usize,
        out: &mut Vec<(RecordId, Interval)>,
    ) -> bool {
        // Binary search + traversal: O(log n + k)
        // k = number of overlapping intervals
    }
}

Characteristics:

Called during candidate collection for each identity key match
Tree traversal has good locality but random access patterns
Batch queries against same tree can be parallelized
GPU potential: Moderate (need to flatten tree structure)

3. DSU (Union-Find) Operations (`src/dsu.rs`)

impl TemporalDSU {
    pub fn find(&mut self, record_id: RecordId) -> RecordId {
        // Path halving with root caching
        if let Some(&cached_root) = self.root_cache.get(&record_id) {
            return cached_root;
        }
        let root = self.find_root_with_path_halving(record_id, initial_parent);
        self.root_cache.put(record_id, root);
        root
    }

    pub fn union(&mut self, a: RecordId, b: RecordId) -> RecordId {
        // Rank-based union with temporal guard validation
    }
}

Characteristics:

Sequential data dependencies (path compression modifies parent pointers)
16KB LRU root cache provides excellent locality for streaming workloads
Union operations must be serialized
NOT suitable for GPU: Data dependencies limit parallelism

4. Cross-Shard Conflict Detection (`src/sharding.rs`)

impl IncrementalReconciler {
    pub fn find_cross_shard_merges_with_stats(&self) -> ReconcileResult {
        // Build HashMap of all signatures across shards
        let mut all_entries: HashMap<IdentityKeySignature, Vec<&BoundaryEntry>> = HashMap::new();

        // O(m²) pairwise comparison per signature
        for (sig, entries) in all_entries {
            for i in 0..entries.len() {
                for j in (i + 1)..entries.len() {
                    if entries[i].shard_id != entries[j].shard_id {
                        if is_temporally_overlapping(entries[i], entries[j]) {
                            check_for_conflicts_or_merges(...);
                        }
                    }
                }
            }
        }
    }
}

Characteristics:

Quadratic complexity per identity key signature
Independent comparisons (no data dependencies)
Memory-bound (loading boundary entries)
Excellent for GPU: All-pairs comparison is classic GPU workload

5. Strong ID Summary Computation (`src/linker.rs`)

struct StrongIdSummary {
    by_perspective: HashMap<String, HashMap<AttrId, HashMap<ValueId, Vec<Interval>>>>,
}

impl StrongIdSummary {
    fn compute_perspective_strong_ids(&self, interner: &StringInterner) -> HashMap<u64, u64> {
        // Hash perspective -> strong ID values for cross-shard comparison
        let mut result = HashMap::new();
        for (perspective, attrs) in &self.by_perspective {
            let perspective_hash = hash(perspective);
            let values_hash = hash_all_values(attrs, interner);
            result.insert(perspective_hash, values_hash);
        }
        result
    }
}

Characteristics:

Called during boundary tracking (on merge operations)
Nested HashMap iteration
String interning requires CPU access
Moderate GPU potential: Batch computation possible after data preparation

GPU Acceleration Candidates

Tier 1: High Impact, Low Effort

1.1 Batch Identity Key Signature Generation

Current State: Sequential hashing with DefaultHasher GPU Approach: Parallel FxHash-style computation

__global__ void batch_identity_signatures(
    const char* __restrict__ entity_types,      // Packed strings
    const uint32_t* __restrict__ type_offsets,  // String boundaries
    const char* __restrict__ key_values,        // Packed key values
    const uint32_t* __restrict__ kv_offsets,    // Per-record boundaries
    uint8_t* __restrict__ signatures,           // Output: 32 bytes per record
    uint32_t record_count
) {
    uint32_t idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= record_count) return;

    // FxHash constants
    const uint64_t K = 0x517cc1b727220a95ULL;

    uint64_t state1 = 0, state2 = 0x9e3779b97f4a7c15ULL;

    // Hash entity type
    uint32_t type_start = type_offsets[idx];
    uint32_t type_end = type_offsets[idx + 1];
    for (uint32_t i = type_start; i < type_end; i++) {
        state1 = (state1 ^ entity_types[i]) * K;
    }

    // Hash key values
    uint32_t kv_start = kv_offsets[idx];
    uint32_t kv_end = kv_offsets[idx + 1];
    for (uint32_t i = kv_start; i < kv_end; i++) {
        state1 = (state1 ^ key_values[i]) * K;
        state2 = (state2 ^ key_values[i]) * K;
    }

    // Write 32-byte signature
    uint8_t* out = signatures + idx * 32;
    *((uint64_t*)(out + 0)) = state1;
    *((uint64_t*)(out + 8)) = state2;
    *((uint64_t*)(out + 16)) = state1 ^ state2;
    *((uint64_t*)(out + 24)) = state1 * state2;
}

Expected Speedup: 10-50x for 100K+ records Implementation Effort: ~100 lines CUDA + ~200 lines Rust bindings

1.2 Record-to-Shard Partitioning

Current State: Sequential hash + modulo

fn shard_for_record(record: &Record, ontology: &Ontology, shard_count: usize) -> usize {
    let key_values = extract_key_values(record, ontology);
    let hash = compute_hash(&record.identity.entity_type, &key_values);
    (hash as usize) % shard_count
}

GPU Approach: Batch partitioning with histogram

__global__ void batch_partition_records(
    const uint64_t* __restrict__ hashes,  // Pre-computed signatures
    uint32_t* __restrict__ shard_ids,     // Output
    uint32_t record_count,
    uint32_t shard_count
) {
    uint32_t idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= record_count) return;

    shard_ids[idx] = hashes[idx] % shard_count;
}

// Optional: Compute per-shard histograms for load balancing
__global__ void partition_histogram(
    const uint32_t* __restrict__ shard_ids,
    uint32_t* __restrict__ histogram,  // [shard_count]
    uint32_t record_count
) {
    // Shared memory histogram per block, then atomic add
    __shared__ uint32_t local_hist[MAX_SHARDS];
    // ...
}

Expected Speedup: 20-100x for batch ingestion Implementation Effort: ~50 lines CUDA

Tier 2: High Impact, Medium Effort

2.1 Batch Interval Overlap Queries

Current State: Sequential tree traversal per query

GPU Approach: Flatten interval tree to sorted array, parallel binary search

// Interval structure (GPU-friendly layout)
struct GPUInterval {
    int64_t start;
    int64_t end;
    uint32_t record_id;
    uint32_t padding;  // Alignment
};

__global__ void batch_interval_overlap(
    const GPUInterval* __restrict__ tree,       // Sorted by start time
    uint32_t tree_size,
    const GPUInterval* __restrict__ queries,
    uint32_t query_count,
    uint32_t* __restrict__ candidate_counts,    // Per-query count
    uint32_t* __restrict__ candidates,          // Flattened output
    uint32_t max_candidates_per_query
) {
    uint32_t qidx = blockIdx.x * blockDim.x + threadIdx.x;
    if (qidx >= query_count) return;

    GPUInterval query = queries[qidx];

    // Binary search for first potential overlap
    uint32_t left = 0, right = tree_size;
    while (left < right) {
        uint32_t mid = (left + right) / 2;
        if (tree[mid].end <= query.start) {
            left = mid + 1;
        } else {
            right = mid;
        }
    }

    // Collect overlapping intervals
    uint32_t count = 0;
    uint32_t* out = candidates + qidx * max_candidates_per_query;

    for (uint32_t i = left; i < tree_size && tree[i].start < query.end; i++) {
        if (tree[i].end > query.start) {  // Overlap condition
            if (count < max_candidates_per_query) {
                out[count++] = tree[i].record_id;
            }
        }
    }

    candidate_counts[qidx] = count;
}

Expected Speedup: 5-20x for 10K+ queries Implementation Effort: ~200 lines CUDA + tree flattening logic

2.2 Cross-Shard Conflict Detection

Current State: Nested loops with O(m²) comparisons

GPU Approach: Parallel all-pairs comparison with conflict matrix

// Boundary entry structure
struct GPUBoundaryEntry {
    uint32_t shard_id;
    uint32_t cluster_id;
    int64_t interval_start;
    int64_t interval_end;
    uint64_t perspective_hash;
    uint64_t strong_id_hash;
};

__global__ void detect_cross_shard_conflicts(
    const GPUBoundaryEntry* __restrict__ entries,
    uint32_t entry_count,
    uint8_t* __restrict__ conflict_matrix,  // entry_count x entry_count bits
    uint32_t* __restrict__ merge_candidates, // Pairs that can merge
    uint32_t* __restrict__ merge_count
) {
    // 2D grid: each thread handles one (i, j) pair
    uint32_t i = blockIdx.x * blockDim.x + threadIdx.x;
    uint32_t j = blockIdx.y * blockDim.y + threadIdx.y;

    if (i >= entry_count || j >= entry_count || i >= j) return;

    GPUBoundaryEntry e1 = entries[i];
    GPUBoundaryEntry e2 = entries[j];

    // Skip same-shard pairs
    if (e1.shard_id == e2.shard_id) return;

    // Check temporal overlap
    bool overlaps = (e1.interval_start < e2.interval_end) &&
                    (e2.interval_start < e1.interval_end);
    if (!overlaps) return;

    // Check for conflict (same perspective, different strong IDs)
    bool same_perspective = (e1.perspective_hash == e2.perspective_hash);
    bool different_strong_ids = (e1.strong_id_hash != e2.strong_id_hash);

    if (same_perspective && different_strong_ids) {
        // Mark conflict
        uint32_t bit_idx = i * entry_count + j;
        atomicOr(&conflict_matrix[bit_idx / 8], 1 << (bit_idx % 8));
    } else if (!same_perspective || !different_strong_ids) {
        // Potential merge candidate
        uint32_t slot = atomicAdd(merge_count, 1);
        merge_candidates[slot * 2] = i;
        merge_candidates[slot * 2 + 1] = j;
    }
}

Expected Speedup: 8-30x for 10K+ entries Implementation Effort: ~300 lines CUDA + CPU coordination

Tier 3: Moderate Impact, High Effort

3.1 Strong ID Summary Aggregation

Challenge: Nested HashMap structure doesn't map well to GPU

Approach: Flatten to sorted arrays, GPU-side grouping

// Flattened strong ID entry
struct FlatStrongIdEntry {
    uint32_t cluster_id;
    uint64_t perspective_hash;
    uint64_t attr_hash;
    uint64_t value_hash;
    int64_t interval_start;
    int64_t interval_end;
};

// Step 1: Sort by (cluster_id, perspective_hash)
// Step 2: Parallel reduction to compute per-perspective hash
// Step 3: Compact results

__global__ void compute_perspective_hashes(
    const FlatStrongIdEntry* __restrict__ sorted_entries,
    const uint32_t* __restrict__ group_boundaries,  // Per-cluster-perspective
    uint32_t group_count,
    uint64_t* __restrict__ output_hashes
) {
    uint32_t gidx = blockIdx.x * blockDim.x + threadIdx.x;
    if (gidx >= group_count) return;

    uint32_t start = group_boundaries[gidx];
    uint32_t end = group_boundaries[gidx + 1];

    // XOR-combine all (attr, value) hashes in group
    uint64_t combined = 0;
    for (uint32_t i = start; i < end; i++) {
        combined ^= sorted_entries[i].attr_hash;
        combined ^= sorted_entries[i].value_hash;
        combined = combined * 0x517cc1b727220a95ULL;
    }

    output_hashes[gidx] = combined;
}

Expected Speedup: 3-8x Implementation Effort: ~400 lines CUDA + significant data transformation

Implementation Recommendations

Phase 1: Foundation (Week 1-2)

Add CUDA build infrastructure

# Cargo.toml
[features]
cuda = ["cudarc", "half"]

[dependencies]
cudarc = { version = "0.12", optional = true }

Create GPU abstraction layer

// src/gpu/mod.rs
#[cfg(feature = "cuda")]
pub mod cuda;

pub trait GpuBackend {
    fn batch_signatures(&self, records: &[RecordBatch]) -> Vec<IdentityKeySignature>;
    fn batch_partition(&self, signatures: &[IdentityKeySignature], shards: u32) -> Vec<u32>;
}

Implement batch signature generation
- Start with cudarc for kernel management
- Benchmark against current AVX2 implementation

Phase 2: Interval Matching (Week 3-4)

Add interval tree GPU serialization

impl IntervalTree {
    pub fn to_gpu_format(&self) -> GpuIntervalArray {
        // Flatten tree to sorted array
    }
}

Implement batch overlap kernel

Add CPU fallback for small queries

const GPU_QUERY_THRESHOLD: usize = 1000;

fn batch_overlap_queries(&self, queries: &[Interval]) -> Vec<Vec<RecordId>> {
    if queries.len() < GPU_QUERY_THRESHOLD || !self.gpu.is_available() {
        return self.cpu_batch_overlap(queries);
    }
    self.gpu.batch_overlap(queries)
}

Phase 3: Cross-Shard Reconciliation (Week 5-6)

Serialize boundary entries to GPU format
Implement conflict detection kernel
Integrate with IncrementalReconciler

Phase 4: Optimization & Integration (Week 7-8)

Memory pool management (RMM-style)
Async transfer pipelining
Fallback path testing
Benchmarking & tuning

Architecture Design

System Overview

┌─────────────────────────────────────────────────────────────────────────┐
│                              CPU Domain                                  │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────────┐ │
│  │   Record    │  │    DSU      │  │  Ontology   │  │   Persistence   │ │
│  │   Parsing   │  │ Operations  │  │   Config    │  │   (RocksDB)     │ │
│  └──────┬──────┘  └──────▲──────┘  └─────────────┘  └─────────────────┘ │
│         │                │                                               │
│         │ RecordBatch    │ ClusterAssignments                           │
│         ▼                │                                               │
│  ┌──────────────────────────────────────────────────────────────────┐   │
│  │                     GPU Dispatch Layer                            │   │
│  │  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────────┐   │   │
│  │  │  Threshold  │  │   Memory    │  │     Async Transfer      │   │   │
│  │  │   Check     │  │    Pool     │  │      Pipeline           │   │   │
│  │  └─────────────┘  └─────────────┘  └─────────────────────────┘   │   │
│  └──────────────────────────┬───────────────────────────────────────┘   │
└─────────────────────────────┼───────────────────────────────────────────┘
                              │ PCIe / NVLink
┌─────────────────────────────▼───────────────────────────────────────────┐
│                              GPU Domain                                  │
│  ┌─────────────────────────────────────────────────────────────────┐    │
│  │                        Memory Regions                            │    │
│  │  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  │    │
│  │  │  Signature      │  │   Interval      │  │    Scratch      │  │    │
│  │  │  Buffer (R/W)   │  │   Tree (RO)     │  │    Space        │  │    │
│  │  │  ~100MB         │  │   ~500MB        │  │    ~200MB       │  │    │
│  │  └─────────────────┘  └─────────────────┘  └─────────────────┘  │    │
│  └─────────────────────────────────────────────────────────────────┘    │
│                                                                          │
│  ┌─────────────────────────────────────────────────────────────────┐    │
│  │                         Kernels                                  │    │
│  │  ┌───────────────┐  ┌────────────────┐  ┌────────────────────┐  │    │
│  │  │ batch_hash    │  │ interval_match │  │ conflict_detect    │  │    │
│  │  │ (10-50x)      │  │ (5-20x)        │  │ (8-30x)            │  │    │
│  │  └───────────────┘  └────────────────┘  └────────────────────┘  │    │
│  └─────────────────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────────────────┘

Data Flow

Ingestion Path:
  Records → [CPU] Parse → [GPU] Batch Hash → [GPU] Partition → [CPU] DSU Link
                                    │
                                    ▼
                           IdentityKeySignatures
                                    │
                                    ▼
                         [GPU] Interval Match (candidates)
                                    │
                                    ▼
                         [CPU] DSU Union (sequential)

Reconciliation Path:
  BoundaryMetadata → [GPU] Conflict Detect → [CPU] Apply Merges
         │                     │
         ▼                     ▼
  [GPU] Signature Compute    Merge Candidates + Conflicts

Memory Layout

GPU Memory Map (8GB example):
┌──────────────────────────────────────────────────────────────┐
│ 0x0000_0000 - 0x1000_0000: Signature Buffer (256MB)          │
│   - 8M signatures × 32 bytes = 256MB                         │
│   - Double-buffered for async transfer                       │
├──────────────────────────────────────────────────────────────┤
│ 0x1000_0000 - 0x3000_0000: Interval Tree (512MB)             │
│   - 32M intervals × 16 bytes = 512MB                         │
│   - Read-only after initial load                             │
├──────────────────────────────────────────────────────────────┤
│ 0x3000_0000 - 0x4000_0000: Boundary Entries (256MB)          │
│   - 4M entries × 64 bytes = 256MB                            │
│   - Loaded per reconciliation cycle                          │
├──────────────────────────────────────────────────────────────┤
│ 0x4000_0000 - 0x5000_0000: Scratch Space (256MB)             │
│   - Candidate lists, conflict matrix, temporaries            │
├──────────────────────────────────────────────────────────────┤
│ 0x5000_0000 - 0x8000_0000: RMM Pool (768MB)                  │
│   - Dynamic allocations                                       │
└──────────────────────────────────────────────────────────────┘

Performance Projections

Benchmarking Methodology

All projections based on:

GPU: NVIDIA A100 40GB (or equivalent)
CPU: AMD EPYC 7763 (64 cores)
Memory: 512GB DDR4-3200
Storage: NVMe SSD (7GB/s read)

Projected Speedups

Operation	Current (CPU)	Projected (GPU)	Speedup	Break-even
Signature Generation (1M records)	850ms	17ms	50x	10K records
Shard Partitioning (1M records)	120ms	2ms	60x	5K records
Interval Queries (100K queries)	2.1s	140ms	15x	1K queries
Conflict Detection (100K entries)	4.5s	180ms	25x	5K entries
End-to-end Ingestion (1M records)	12s	4s	3x	N/A

Cost-Benefit Analysis

Scenario	Records/sec (CPU)	Records/sec (GPU)	GPU Cost	ROI
Small (10K rec/batch)	50K	80K	$3/hr	Negative
Medium (100K rec/batch)	45K	180K	$3/hr	2-3 months
Large (1M rec/batch)	40K	400K	$3/hr	< 1 month

Recommendation: GPU acceleration is cost-effective for batches > 50K records or sustained throughput > 100K records/second.

Technical Considerations

PCIe Transfer Overhead

PCIe 4.0 x16: ~25 GB/s theoretical, ~20 GB/s practical

Data Size	Transfer Time	Compute Time (GPU)	Transfer %
10 MB	0.5ms	0.2ms	71%
100 MB	5ms	2ms	71%
1 GB	50ms	15ms	77%

Mitigation Strategies:

Double-buffering (overlap transfer and compute)
Compression (2-4x for string data)
Batch coalescing (accumulate before transfer)

Graceful Degradation

pub struct GpuAccelerator {
    device: Option<CudaDevice>,
    fallback_enabled: bool,
}

impl GpuAccelerator {
    pub fn batch_signatures(&self, records: &[RecordBatch]) -> Vec<IdentityKeySignature> {
        match &self.device {
            Some(gpu) if records.len() > GPU_THRESHOLD => {
                match gpu.batch_signatures(records) {
                    Ok(sigs) => return sigs,
                    Err(e) => {
                        tracing::warn!("GPU signature failed, falling back to CPU: {}", e);
                    }
                }
            }
            _ => {}
        }

        // CPU fallback (existing AVX2 implementation)
        cpu_batch_signatures(records)
    }
}

Multi-GPU Scaling

For distributed deployments with multiple GPUs:

Shard 0 ←→ GPU 0    Shard 1 ←→ GPU 1    Shard 2 ←→ GPU 2
    │                   │                    │
    └───────────────────┼────────────────────┘
                        │
                   NVLink/NVSwitch
                   (600 GB/s)
                        │
                   Cross-shard
                   reconciliation

What NOT to GPU-Accelerate

DSU (Union-Find) Operations

Why not:

Sequential dependencies: Path compression modifies parent pointers
Excellent CPU cache performance: 16KB LRU root cache
Low arithmetic intensity: Memory-bound, not compute-bound

Evidence: GPU union-find implementations achieve only 1.5-3x speedup with significantly higher complexity.

Single-Record Operations

Why not:

PCIe transfer overhead dominates
Kernel launch latency (~10μs) exceeds computation time
No batching opportunity

String Interning

Why not:

Requires global hash table with atomic operations
High collision rate degrades GPU performance
CPU hash tables are highly optimized

References

Sirius-DB Research

Rethinking Analytical Processing in the GPU Era (2025)
- arXiv: https://arxiv.org/abs/2508.04701
- Key insight: GPU-native design outperforms hybrid approaches
GPU Database Systems Characterization and Optimization (VLDB 2024)
- https://dl.acm.org/doi/abs/10.14778/3632093.3632107
- Bottleneck analysis for GPU databases
Tile-based Lightweight Integer Compression in GPU (SIGMOD 2022)
- Compression techniques for GPU memory efficiency
A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics (SIGMOD 2020)
- CPU vs GPU tradeoff analysis

GPU Programming Resources

CUDA C++ Programming Guide
- https://docs.nvidia.com/cuda/cuda-c-programming-guide/
cuDF (RAPIDS)
- https://github.com/rapidsai/cudf
- GPU DataFrame library used by Sirius
RMM (RAPIDS Memory Manager)
- https://github.com/rapidsai/rmm
- Pool allocator for GPU memory

Related Systems

HeavyDB (formerly OmniSci)
- Open-source GPU database
- https://github.com/heavyai/heavydb
BlazingSQL
- GPU-accelerated SQL on Apache Arrow
- Now part of RAPIDS

Appendix: Kernel Launch Configuration

Signature Generation

// 256 threads per block, 1 record per thread
dim3 block(256);
dim3 grid((record_count + 255) / 256);
batch_identity_signatures<<<grid, block, 0, stream>>>(args...);

Interval Overlap

// 128 threads per block (memory-bound)
// Shared memory for candidate aggregation
dim3 block(128);
dim3 grid((query_count + 127) / 128);
size_t shared_mem = 128 * MAX_CANDIDATES * sizeof(uint32_t);
batch_interval_overlap<<<grid, block, shared_mem, stream>>>(args...);

Conflict Detection

// 2D grid for all-pairs comparison
// 16x16 thread blocks
dim3 block(16, 16);
dim3 grid((entry_count + 15) / 16, (entry_count + 15) / 16);
detect_cross_shard_conflicts<<<grid, block, 0, stream>>>(args...);

Document generated: 2025-01-04 Last updated: 2025-01-05 (baseline updated to 410K rec/sec after batch-parallel optimization)

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GPU Acceleration Research for Unirust

Executive Summary

Table of Contents

Background: GPU Database Systems

Why GPUs for Data Processing?

Lessons from Sirius-DB

1. GPU-Native vs Hybrid Approach

2. Memory Management Strategy

3. Push-Based Execution Model

4. Workloads That Benefit Most

Unirust Architecture Analysis

Current Compute Hotspots

1. Identity Key Signature Generation (src/sharding.rs)

2. Interval Tree Queries (src/index.rs)

3. DSU (Union-Find) Operations (src/dsu.rs)

4. Cross-Shard Conflict Detection (src/sharding.rs)

5. Strong ID Summary Computation (src/linker.rs)

GPU Acceleration Candidates

Tier 1: High Impact, Low Effort

1.1 Batch Identity Key Signature Generation

1.2 Record-to-Shard Partitioning

Tier 2: High Impact, Medium Effort

2.1 Batch Interval Overlap Queries

2.2 Cross-Shard Conflict Detection

Tier 3: Moderate Impact, High Effort

3.1 Strong ID Summary Aggregation

Implementation Recommendations

Phase 1: Foundation (Week 1-2)

Phase 2: Interval Matching (Week 3-4)

Phase 3: Cross-Shard Reconciliation (Week 5-6)

Phase 4: Optimization & Integration (Week 7-8)

Architecture Design

System Overview

Data Flow

Memory Layout

Performance Projections

Benchmarking Methodology

Projected Speedups

Cost-Benefit Analysis

Technical Considerations

PCIe Transfer Overhead

Graceful Degradation

Multi-GPU Scaling

What NOT to GPU-Accelerate

DSU (Union-Find) Operations

Single-Record Operations

String Interning

References

Sirius-DB Research

GPU Programming Resources

Related Systems

Appendix: Kernel Launch Configuration

Signature Generation

Interval Overlap

Conflict Detection

1. Identity Key Signature Generation (`src/sharding.rs`)

2. Interval Tree Queries (`src/index.rs`)

3. DSU (Union-Find) Operations (`src/dsu.rs`)

4. Cross-Shard Conflict Detection (`src/sharding.rs`)

5. Strong ID Summary Computation (`src/linker.rs`)