trueno_db/gpu/
multigpu.rs

1//! Multi-GPU data partitioning and distribution
2//!
3//! Toyota Way Principles:
4//! - Heijunka (Load Leveling): Distribute work evenly across GPUs
5//! - Muda elimination: Parallel execution reduces total wall-clock time
6//!
7//! Architecture:
8//! - Detect all available GPU devices
9//! - Partition data by range (contiguous chunks) or hash (random distribution)
10//! - Execute operations in parallel across all GPUs
11//! - Reduce results from all GPUs to final answer
12//!
13//! References:
14//! - Leis et al. (2014): Morsel-driven parallelism for NUMA systems
15//! - `MapD` (2017): Multi-GPU query execution patterns
16
17use crate::{Error, Result};
18use arrow::array::Int32Array;
19use wgpu;
20
21/// Information about a single GPU device
22#[derive(Debug, Clone)]
23pub struct GpuDeviceInfo {
24    /// Device name (e.g., "NVIDIA RTX 4090", "AMD Radeon RX 7900 XTX")
25    pub name: String,
26    /// Device type (`DiscreteGpu`, `IntegratedGpu`, `VirtualGpu`, Cpu, Other)
27    pub device_type: wgpu::DeviceType,
28    /// Backend (Vulkan, Metal, DX12, DX11, GL, `BrowserWebGPU`)
29    pub backend: wgpu::Backend,
30}
31
32/// Multi-GPU device manager
33pub struct MultiGpuManager {
34    /// All available GPU devices
35    devices: Vec<GpuDeviceInfo>,
36}
37
38impl MultiGpuManager {
39    /// Detect all available GPU devices
40    ///
41    /// # Errors
42    /// Returns error if GPU enumeration fails
43    pub fn new() -> Result<Self> {
44        let instance = wgpu::Instance::new(wgpu::InstanceDescriptor {
45            backends: wgpu::Backends::all(),
46            ..Default::default()
47        });
48
49        // Enumerate all adapters
50        let adapters = instance.enumerate_adapters(wgpu::Backends::all());
51
52        // Convert adapters to device info
53        let devices: Vec<GpuDeviceInfo> = adapters
54            .iter()
55            .map(|adapter| {
56                let info = adapter.get_info();
57                GpuDeviceInfo {
58                    name: info.name,
59                    device_type: info.device_type,
60                    backend: info.backend,
61                }
62            })
63            .collect();
64
65        Ok(Self { devices })
66    }
67
68    /// Get number of available GPUs
69    #[must_use]
70    pub fn device_count(&self) -> usize {
71        self.devices.len()
72    }
73
74    /// Get information about all devices
75    #[must_use]
76    pub fn devices(&self) -> &[GpuDeviceInfo] {
77        &self.devices
78    }
79}
80
81/// Data partitioning strategy
82#[derive(Debug, Clone, Copy, PartialEq, Eq)]
83pub enum PartitionStrategy {
84    /// Range partitioning: divide data into contiguous chunks
85    /// Example: GPU0: [0..N/2], GPU1: [N/2..N]
86    Range,
87    /// Hash partitioning: distribute rows based on `hash(row_id)` % `num_gpus`
88    /// Better load balancing for skewed data
89    Hash,
90}
91
92/// Data partition for a single GPU
93#[derive(Debug)]
94pub struct DataPartition {
95    /// GPU device index
96    pub device_id: usize,
97    /// Data chunk for this GPU
98    pub data: Int32Array,
99}
100
101/// Partition data across multiple GPUs
102///
103/// # Arguments
104/// * `data` - Input array to partition
105/// * `num_partitions` - Number of partitions (typically number of GPUs)
106/// * `strategy` - Partitioning strategy (Range or Hash)
107///
108/// # Returns
109/// Vector of partitions, one per GPU
110///
111/// # Errors
112/// Returns error if partitioning fails
113pub fn partition_data(
114    data: &Int32Array,
115    num_partitions: usize,
116    strategy: PartitionStrategy,
117) -> Result<Vec<DataPartition>> {
118    if num_partitions == 0 {
119        return Err(Error::InvalidInput("num_partitions must be > 0".to_string()));
120    }
121
122    let partitions = match strategy {
123        PartitionStrategy::Range => partition_range(data, num_partitions),
124        PartitionStrategy::Hash => partition_hash(data, num_partitions),
125    };
126    Ok(partitions)
127}
128
129/// Partition data using range partitioning (contiguous chunks)
130fn partition_range(data: &Int32Array, num_partitions: usize) -> Vec<DataPartition> {
131    let len = data.len();
132    let mut partitions = Vec::with_capacity(num_partitions);
133
134    // Calculate chunk size (handle uneven division)
135    let base_size = len / num_partitions;
136    let remainder = len % num_partitions;
137
138    let mut offset = 0;
139    for device_id in 0..num_partitions {
140        // First 'remainder' partitions get an extra element
141        let size = if device_id < remainder { base_size + 1 } else { base_size };
142
143        // Extract slice
144        let values: Vec<i32> = (offset..offset + size).map(|i| data.value(i)).collect();
145
146        partitions.push(DataPartition { device_id, data: Int32Array::from(values) });
147
148        offset += size;
149    }
150
151    partitions
152}
153
154/// Partition data using hash partitioning (random distribution)
155fn partition_hash(data: &Int32Array, num_partitions: usize) -> Vec<DataPartition> {
156    use std::collections::hash_map::DefaultHasher;
157    use std::hash::{Hash, Hasher};
158
159    // Initialize empty vectors for each partition
160    let mut buckets: Vec<Vec<i32>> = (0..num_partitions).map(|_| Vec::new()).collect();
161
162    // Distribute elements by hash
163    for i in 0..data.len() {
164        let value = data.value(i);
165
166        // Hash the row index (not the value) for deterministic distribution
167        let mut hasher = DefaultHasher::new();
168        i.hash(&mut hasher);
169        let hash = hasher.finish();
170
171        #[allow(clippy::cast_possible_truncation)]
172        let partition_id = (hash % num_partitions as u64) as usize;
173        buckets[partition_id].push(value);
174    }
175
176    // Convert buckets to DataPartition
177    let partitions: Vec<DataPartition> = buckets
178        .into_iter()
179        .enumerate()
180        .map(|(device_id, values)| DataPartition { device_id, data: Int32Array::from(values) })
181        .collect();
182
183    partitions
184}
185
186#[cfg(test)]
187mod tests {
188    use super::*;
189
190    #[test]
191    fn test_multigpu_device_detection() {
192        // RED: This test should fail because MultiGpuManager::new() is not implemented
193        let manager = MultiGpuManager::new();
194
195        // If no GPUs available, should return Ok with 0 devices
196        // If GPUs available, should return Ok with device info
197        match manager {
198            Ok(mgr) => {
199                // Should detect at least 0 devices (graceful degradation)
200                let count = mgr.device_count();
201                println!("Detected {count} GPU device(s)");
202
203                // If devices found, validate their info
204                if count > 0 {
205                    for (i, device) in mgr.devices().iter().enumerate() {
206                        println!("GPU {i}: {device:?}");
207                        assert!(!device.name.is_empty(), "Device name should not be empty");
208                    }
209                }
210            }
211            Err(e) => {
212                panic!("MultiGpuManager::new() failed: {e}");
213            }
214        }
215    }
216
217    #[test]
218    fn test_multigpu_device_count_zero_when_no_gpu() {
219        // RED: Should fail because not implemented
220        // When no GPU available, should return 0 devices (not an error)
221        let manager = MultiGpuManager::new();
222
223        if let Ok(mgr) = manager {
224            // Valid result: 0 devices (no GPU) or N devices (GPUs found)
225            // device_count is usize, so always >= 0
226            let _count = mgr.device_count();
227        } else {
228            // Also acceptable: return error if GPU enumeration fails
229            // But prefer returning 0 devices for graceful degradation
230        }
231    }
232
233    #[test]
234    fn test_partition_range_even_split() {
235        // RED: Should fail because partition_data() is not implemented
236        let data = Int32Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8]);
237        let partitions = partition_data(&data, 2, PartitionStrategy::Range).unwrap();
238
239        assert_eq!(partitions.len(), 2);
240
241        // GPU 0: [1, 2, 3, 4]
242        assert_eq!(partitions[0].device_id, 0);
243        assert_eq!(partitions[0].data.len(), 4);
244        assert_eq!(partitions[0].data.value(0), 1);
245        assert_eq!(partitions[0].data.value(3), 4);
246
247        // GPU 1: [5, 6, 7, 8]
248        assert_eq!(partitions[1].device_id, 1);
249        assert_eq!(partitions[1].data.len(), 4);
250        assert_eq!(partitions[1].data.value(0), 5);
251        assert_eq!(partitions[1].data.value(3), 8);
252    }
253
254    #[test]
255    fn test_partition_range_uneven_split() {
256        // RED: Should fail because not implemented
257        // With 10 elements and 3 GPUs: [4, 3, 3] or [3, 3, 4] distribution
258        let data = Int32Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
259        let partitions = partition_data(&data, 3, PartitionStrategy::Range).unwrap();
260
261        assert_eq!(partitions.len(), 3);
262
263        // Verify all data is partitioned (no data loss)
264        let total_len: usize = partitions.iter().map(|p| p.data.len()).sum();
265        assert_eq!(total_len, 10);
266
267        // Verify partitions are contiguous
268        assert_eq!(partitions[0].data.value(0), 1); // First partition starts at 1
269        let last_partition = &partitions[2];
270        assert_eq!(last_partition.data.value(last_partition.data.len() - 1), 10);
271        // Last partition ends at 10
272    }
273
274    #[test]
275    fn test_partition_hash_distribution() {
276        // RED: Should fail because not implemented
277        let data = Int32Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8]);
278        let partitions = partition_data(&data, 2, PartitionStrategy::Hash).unwrap();
279
280        assert_eq!(partitions.len(), 2);
281
282        // Verify all data is partitioned (no data loss)
283        let total_len: usize = partitions.iter().map(|p| p.data.len()).sum();
284        assert_eq!(total_len, 8);
285
286        // Hash partitioning: elements may be in different order
287        // Just verify device IDs are correct
288        assert_eq!(partitions[0].device_id, 0);
289        assert_eq!(partitions[1].device_id, 1);
290    }
291
292    #[test]
293    fn test_partition_single_gpu() {
294        // RED: Should fail because not implemented
295        // With 1 GPU, all data goes to partition 0
296        let data = Int32Array::from(vec![1, 2, 3, 4]);
297        let partitions = partition_data(&data, 1, PartitionStrategy::Range).unwrap();
298
299        assert_eq!(partitions.len(), 1);
300        assert_eq!(partitions[0].device_id, 0);
301        assert_eq!(partitions[0].data.len(), 4);
302    }
303
304    #[test]
305    fn test_partition_empty_data() {
306        // RED: Should fail because not implemented
307        let data = Int32Array::from(vec![] as Vec<i32>);
308        let partitions = partition_data(&data, 2, PartitionStrategy::Range).unwrap();
309
310        assert_eq!(partitions.len(), 2);
311        // Both partitions should be empty
312        assert_eq!(partitions[0].data.len(), 0);
313        assert_eq!(partitions[1].data.len(), 0);
314    }
315
316    #[test]
317    fn test_partition_zero_partitions_error() {
318        // RED: Should fail because not implemented
319        // num_partitions = 0 should return error
320        let data = Int32Array::from(vec![1, 2, 3]);
321        let result = partition_data(&data, 0, PartitionStrategy::Range);
322
323        assert!(result.is_err());
324        assert!(result.unwrap_err().to_string().contains("num_partitions must be > 0"));
325    }
326}
trueno_db/gpu/multigpu.rs

trueno_db/gpu/
multigpu.rs
