Scaling K-Nearest Neighbors with Dual NVIDIA RTX 3090 GPUs: A Complete Guide

Introduction

The K-Nearest Neighbors (KNN) algorithm is one of the most intuitive and widely-used machine learning algorithms, but it faces significant challenges when dealing with massive datasets. With the computational power of dual NVIDIA RTX 3090 GPUs, we can overcome these limitations and process enormous datasets efficiently. This guide explores how to harness this power for large-scale KNN implementations.

Understanding K-Nearest Neighbors (KNN)

What is KNN?

KNN is a lazy learning algorithm that makes predictions based on the k closest training examples in the feature space. Unlike other algorithms that learn a model during training, KNN stores all training data and performs computations during prediction time.

How KNN Works

1. Distance Calculation: For each query point, calculate distances to all training points
2. Neighbor Selection: Identify the k nearest neighbors based on these distances
3. Prediction:

• Classification: Vote among the k neighbors (majority class wins)
• Regression: Average the target values of the k neighbors

Key Characteristics

• Non-parametric: Makes no assumptions about data distribution
• Instance-based: Uses actual training instances for predictions
• Lazy learning: No explicit training phase
• Memory-intensive: Stores entire training dataset

The Computational Challenge

Why KNN Struggles with Large Datasets

KNN’s computational complexity is O(n×d) for each prediction, where n is the number of training samples and d is the number of features. This creates several challenges:

• Memory Requirements: Storing millions of high-dimensional vectors
• Distance Computations: Calculating distances between query and all training points
• Search Efficiency: Finding k nearest neighbors efficiently
• Scalability: Performance degrades linearly with dataset size

NVIDIA RTX 3090: A Powerhouse for KNN

Technical Specifications

• CUDA Cores: 10,496 per GPU (20,992 total with dual setup)
• Memory: 24GB GDDR6X per GPU (48GB total)
• Memory Bandwidth: 936 GB/s per GPU
• Tensor Cores: 328 per GPU for mixed-precision operations
• Multi-GPU Communication: NVLink for high-speed inter-GPU communication

Why RTX 3090 is Ideal for KNN

1. Massive Parallel Processing: Thousands of cores handle distance calculations simultaneously
2. Large Memory Capacity: 48GB combined memory stores huge datasets
3. High Memory Bandwidth: Fast data access for distance computations
4. Mixed Precision Support: Tensor cores accelerate calculations with FP16/FP32

Implementing KNN with Dual RTX 3090 GPUs

Environment Setup

import numpy as np
import cupy as cp
import cuml
from cuml.neighbors import NearestNeighbors
from cuml.dask.neighbors import NearestNeighbors as DaskNearestNeighbors
import dask.array as da
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import time

Setting Up Multi-GPU Environment

# Initialize CUDA cluster with both GPUs
cluster = LocalCUDACluster(
    n_workers=2,  # One worker per GPU
    threads_per_worker=1,
    memory_limit='20GB',  # Leave some memory for system
    device_memory_limit='20GB'
)
client = Client(cluster)

# Verify GPU setup
print("Available GPUs:", cp.cuda.runtime.getDeviceCount())
for i in range(cp.cuda.runtime.getDeviceCount()):
    cp.cuda.runtime.setDevice(i)
    print(f"GPU {i}: {cp.cuda.runtime.getDeviceProperties(i)['name']}")

Data Preparation and Distribution

def create_large_dataset(n_samples=1000000, n_features=100):
    """Create a large synthetic dataset for demonstration"""
    print(f"Creating dataset with {n_samples:,} samples and {n_features} features...")

    X, y = make_classification(
        n_samples=n_samples,
        n_features=n_features,
        n_informative=n_features//2,
        n_redundant=n_features//4,
        n_clusters_per_class=2,
        random_state=42
    )

    return X.astype(np.float32), y

def distribute_data_across_gpus(X, y):
    """Distribute data across multiple GPUs using Dask"""
    # Convert to Dask arrays for distributed processing
    X_dask = da.from_array(X, chunks=(X.shape[0]//2, X.shape[1]))
    y_dask = da.from_array(y, chunks=(y.shape[0]//2,))

    return X_dask, y_dask

GPU-Accelerated KNN Implementation

class DualGPU_KNN:
    def __init__(self, n_neighbors=5, metric='euclidean'):
        self.n_neighbors = n_neighbors
        self.metric = metric
        self.model = None

    def fit(self, X, y):
        """Fit KNN model using distributed GPU processing"""
        print("Fitting KNN model on dual GPUs...")

        # Use cuML's Dask-enabled KNN for multi-GPU support
        self.model = DaskNearestNeighbors(
            n_neighbors=self.n_neighbors,
            metric=self.metric,
            client=client
        )

        # Fit the model with distributed data
        start_time = time.time()
        self.model.fit(X)
        fit_time = time.time() - start_time

        print(f"Model fitted in {fit_time:.2f} seconds")
        self.y_train = y

    def predict(self, X_test):
        """Make predictions using the fitted model"""
        print("Making predictions...")

        start_time = time.time()

        # Find k nearest neighbors
        distances, indices = self.model.kneighbors(X_test)

        # Convert to CuPy arrays for GPU processing
        indices_gpu = cp.asarray(indices.compute())
        y_train_gpu = cp.asarray(self.y_train.compute())

        # Perform voting for classification
        predictions = []
        for i in range(indices_gpu.shape[0]):
            neighbor_labels = y_train_gpu[indices_gpu[i]]
            # Simple majority vote
            unique_labels, counts = cp.unique(neighbor_labels, return_counts=True)
            predicted_label = unique_labels[cp.argmax(counts)]
            predictions.append(predicted_label)

        predict_time = time.time() - start_time
        print(f"Predictions completed in {predict_time:.2f} seconds")

        return cp.array(predictions)

Optimized Distance Calculations

def optimized_distance_calculation(X1, X2, metric='euclidean'):
    """Optimized distance calculation using GPU memory management"""

    # Use CuPy for GPU-accelerated computations
    X1_gpu = cp.asarray(X1)
    X2_gpu = cp.asarray(X2)

    if metric == 'euclidean':
        # Vectorized Euclidean distance calculation
        # Using broadcasting for memory efficiency
        distances = cp.sqrt(
            cp.sum((X1_gpu[:, None, :] - X2_gpu[None, :, :]) ** 2, axis=2)
        )
    elif metric == 'manhattan':
        distances = cp.sum(
            cp.abs(X1_gpu[:, None, :] - X2_gpu[None, :, :]), axis=2
        )
    elif metric == 'cosine':
        # Normalize vectors for cosine similarity
        X1_norm = X1_gpu / cp.linalg.norm(X1_gpu, axis=1, keepdims=True)
        X2_norm = X2_gpu / cp.linalg.norm(X2_gpu, axis=1, keepdims=True)

        # Cosine distance = 1 - cosine similarity
        distances = 1 - cp.dot(X1_norm, X2_norm.T)

    return distances

Memory Management for Large Datasets

def batch_process_large_dataset(X_train, y_train, X_test, batch_size=10000):
    """Process large datasets in batches to manage GPU memory"""

    n_test_samples = X_test.shape[0]
    all_predictions = []

    # Process test data in batches
    for i in range(0, n_test_samples, batch_size):
        batch_end = min(i + batch_size, n_test_samples)
        X_batch = X_test[i:batch_end]

        print(f"Processing batch {i//batch_size + 1}/{(n_test_samples-1)//batch_size + 1}")

        # Move batch to GPU
        with cp.cuda.Device(0):  # Use first GPU
            X_batch_gpu = cp.asarray(X_batch)

            # Calculate distances (this could be distributed across GPUs)
            distances = optimized_distance_calculation(X_batch_gpu, cp.asarray(X_train))

            # Find k nearest neighbors
            k_indices = cp.argpartition(distances, kth=5, axis=1)[:, :5]

            # Make predictions
            batch_predictions = []
            for j in range(k_indices.shape[0]):
                neighbor_labels = y_train[k_indices[j]]
                unique_labels, counts = cp.unique(neighbor_labels, return_counts=True)
                predicted_label = unique_labels[cp.argmax(counts)]
                batch_predictions.append(predicted_label)

            all_predictions.extend(batch_predictions)

        # Clear GPU memory
        cp.get_default_memory_pool().free_all_blocks()

    return cp.array(all_predictions)

Performance Optimization Strategies

1. Data Layout Optimization

# Use row-major (C-style) layout for better memory access patterns
X = np.ascontiguousarray(X, dtype=np.float32)

# Consider using half-precision for memory efficiency
X_half = X.astype(np.float16)  # Use with caution for accuracy

2. Approximate Nearest Neighbors

from cuml.neighbors import NearestNeighbors

# Use approximate methods for faster search
knn_approx = NearestNeighbors(
    n_neighbors=5,
    algorithm='auto',  # Let cuML choose the best algorithm
    metric='euclidean'
)

3. Dimensionality Reduction

from cuml.decomposition import PCA

# Reduce dimensionality before KNN
pca = PCA(n_components=50)
X_reduced = pca.fit_transform(X)

Complete Example: Processing a Million-Sample Dataset

def main():
    # Create large dataset
    X, y = create_large_dataset(n_samples=1000000, n_features=100)
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42
    )

    print(f"Training set: {X_train.shape}")
    print(f"Test set: {X_test.shape}")

    # Distribute data across GPUs
    X_train_dask, y_train_dask = distribute_data_across_gpus(X_train, y_train)
    X_test_dask, y_test_dask = distribute_data_across_gpus(X_test, y_test)

    # Initialize and train dual-GPU KNN
    dual_gpu_knn = DualGPU_KNN(n_neighbors=5)
    dual_gpu_knn.fit(X_train_dask, y_train_dask)

    # Make predictions
    predictions = dual_gpu_knn.predict(X_test_dask)

    # Calculate accuracy
    accuracy = cp.mean(predictions == y_test_dask.compute())
    print(f"Accuracy: {accuracy:.4f}")

    # Clean up
    client.close()
    cluster.close()

if __name__ == "__main__":
    main()

Performance Benchmarks

Expected Performance Improvements

• Single GPU vs Dual GPU: 1.5–2x speedup for large datasets
• CPU vs Dual GPU: 10–50x speedup depending on dataset size
• Memory Capacity: Handle datasets up to 40GB (accounting for overhead)

Bottlenecks and Solutions

1. Memory Bandwidth: Use mixed precision (FP16) when possible
2. Inter-GPU Communication: Minimize data transfers between GPUs
3. CPU-GPU Transfers: Keep data on GPU throughout the pipeline

Best Practices

Hardware Optimization

1. Use NVLink: Ensure GPUs are connected via NVLink for faster communication
2. Adequate Cooling: RTX 3090s run hot; ensure proper cooling
3. Power Supply: Ensure sufficient power (850W+ recommended)

Software Optimization

1. Batch Processing: Process data in batches to manage memory
2. Asynchronous Operations: Use CUDA streams for overlapping computation
3. Memory Pooling: Use CuPy’s memory pool to reduce allocation overhead

Data Preprocessing

1. Normalization: Normalize features for better distance calculations
2. Dimensionality Reduction: Use PCA or other techniques for high-dimensional data
3. Data Types: Use float32 instead of float64 for memory efficiency

Conclusion

Dual NVIDIA RTX 3090 GPUs provide exceptional capability for scaling KNN algorithms to massive datasets. With 48GB of combined memory and over 20,000 CUDA cores, these GPUs can handle datasets that would be impossible to process efficiently on CPU alone.

The key to success lies in proper data distribution, memory management, and leveraging GPU-optimized libraries like cuML and CuPy. While the initial setup requires careful consideration of hardware and software configurations, the performance gains make it worthwhile for large-scale machine learning applications.

Remember that KNN’s performance characteristics mean that even with GPU acceleration, the algorithm’s time complexity remains O(n×d) per prediction. For extremely large datasets, consider approximate methods or ensemble approaches that can provide similar accuracy with better scalability.

Additional Resources

• cuML Documentation
• CuPy Documentation
• Dask CUDA Documentation