Optimize High-Performance Computing Storage for Machine Learning at Scale

High-performance computing storage considerations are crucial yet often overlooked in machine learning infrastructure. While there's abundant literature on model architecture and NVIDIA GPU optimization, less attention has been paid to storage systems and their impact on training performance. This is particularly important as modern AI workloads push unprecedented demands on storage infrastructure.

In Part 1 of our Storage Benchmarking Series, we examined the benchmark performance results of CoreWeave Distributed File Storage, machine learning (ML) storage patterns for large-scale training workloads, and CoreWeave’s purpose-built architecture.

While benchmarks provide valuable comparative data, their utility alone is often limited by specific configurations, workload characteristics, and the rapid pace of system updates. 

Here in Part 2, we offer tactical strategies for optimizing your storage performance on the CoreWeave Cloud Platform, focusing on practical insights derived from production workloads. 

6 Storage Optimizations for ML Workloads

The key to achieving optimal performance lies in implementing several critical strategies that work together to efficiently feed your GPUs with data. Let's explore these strategies and understand how they work together in a production environment.

The Foundation: Buffering and Caching

At the heart of efficient ML storage access is a robust buffering and caching strategy. Buffering is a short-term holding area for data in transit. Think of caching as creating a temporary staging area for your frequently or recently accessed data, reducing the number of times you need to access the storage system directly. 

Modern ML systems typically implement a multi-level caching strategy, with data flowing from storage through system RAM and potentially into GPU memory. This hierarchy helps manage the trade-off between access speed and capacity.

Here’s how this works in practice:

class HierarchicalCache:
    def __init__(self):
        self.gpu_cache = GPUCache(size='8GB')  # Fastest, smallest
        self.ram_cache = RAMCache(size='128GB')  # Medium speed/size
        self.disk_cache = DiskCache(size='2TB')  # Slowest, largest

    def get_batch(self, indices):
        # Try GPU cache first
        missing = self._fetch_from_cache(indices, self.gpu_cache)
        if not missing:
            return

        # Try RAM cache
        missing = self._fetch_from_cache(missing, self.ram_cache)
        if not missing:
            return

        # Load from disk
        self._load_to_caches(missing)

Conclusion: When implementing buffering, it's crucial to consider your available system resources. Too small a buffer will cause storage to be hit too frequently; too large will risk memory contention with other parts of your training pipeline.

Prefetching: Staying Ahead of the Game

GPU memory bandwidth is precious for AI storage solutions. High-performance computing storage finds ways to effectively use memory to maximize performance while ensuring that memory doesn’t become a bottleneck for workloads.

Prefetching is perhaps the single most important technique for maintaining consistent performance in ML training. The concept is straightforward: while your GPUs are processing the current batch of data, your system should be loading the next batch in the background. However, implementing this effectively requires careful consideration of several factors:

• Making sure that the read doesn’t prefetch too much, as this consumes memory
• Making sure that the prefetches do not overlap with checkpoint writes
• Data layout and access patterns affect the impact of prefetching

Consider this implementation of adaptive prefetching:

class AdaptivePrefetcher:
    def __init__(self):
        self.pattern_history = []
        self.current_strategy = None

    def update_strategy(self):
        # Analyze recent access patterns
        pattern = self.analyze_patterns(self.pattern_history[-1000:])

        if pattern.is_sequential():
            # Aggressive prefetch for sequential access
            self.current_strategy = SequentialStrategy(
                read_ahead=1024,
                max_streams=4
            )
        elif pattern.is_random():
            # Conservative prefetch for random access
            self.current_strategy = RandomStrategy(
                cache_size='64GB',
                prediction_window=100
            )

‍

Conclusion: The key to effective prefetching lies in finding the right balance. Prefetch too little, and your GPUs might end up waiting for data; prefetch too much, and you'll waste memory that could be better used elsewhere.

Data Format and Organization

The choice of data format can have a surprising impact on training performance. While it might be tempting to store your data in a simple, raw format, using optimized formats like TFRecord or WebDataset can significantly improve loading speeds. These formats are designed for efficient streaming and can reduce the overhead of loading many small files.

One common method of data organizing is sharding: dividing a large dataset or database into smaller, more manageable pieces called shards. Each shard contains a subset of the data and can be stored and processed independently across multiple storage nodes or servers. This approach improves scalability, performance, and availability by distributing the data and query load across multiple machines so that there are no hot spots or bottlenecks.

Here's an example of efficient data organization:

class OptimizedDataset:
    def __init__(self, data_path):
        self.shards = self._discover_shards(data_path)
        self.shard_size = '1GB'  # Optimized for network transfer
        self.index = self._build_index()

    def _build_index(self):
        # Create fast lookup index for samples
        index = {}
        for shard in self.shards:
            shard_index = self._read_shard_header(shard)
            index.update(shard_index)
        return index

Conclusion: Proper sharding is essential when dealing with large datasets. The ideal shard size depends on various factors, including your storage system's characteristics and your training pipeline's needs.

Pipeline Optimization: The Big Picture

Data loading pipelines in ML workflows must handle multiple operations efficiently: reading from storage, decompression, preprocessing, and transferring to GPU memory. Each step presents opportunities for optimization. A well-designed pipeline overlaps these operations to minimize idle time.

The following code shows….

class OptimizedDataLoader:
    def __init__(self, dataset_path, batch_size):
        self.dataset_path = dataset_path
        self.batch_size = batch_size
        self.prefetch_queue = Queue(maxsize=3)
        self.background_loader = ThreadPoolExecutor(max_workers=4)

    def _load_batch(self):
        # Load and preprocess data in background
        data = self._read_from_storage()
        processed = self._preprocess(data)
        return self._transfer_to_gpu(processed)

    def start(self):
        # Initialize prefetch queue
        for _ in range(3):
            future = self.background_loader.submit(self._load_batch)
            self.prefetch_queue.put(future)

Conclusion: A well-optimized pipeline with parallelized work streams can significantly reduce training time by ensuring GPUs never wait for data.

Memory Management and Distributed Coordination

As training scales to multiple nodes, coordinating cache contents and prefetch operations becomes critical. The system must balance data locality with network utilization while maintaining efficient memory use across the cluster.

The following code shows…

class DistributedCoordinator:
    def __init__(self, world_size, rank):
        self.cache_directory = {}  # Maps data to node locations
        self.pending_transfers = Queue()
        self.network_monitor = BandwidthMonitor()

    def optimize_data_placement(self):
        # Calculate optimal data placement
        access_patterns = self.collect_global_patterns()
        network_topology = self.network_monitor.get_topology()

        # Optimize based on access frequency and network costs
        new_placement = self.solver.optimize(
            patterns=access_patterns,
            topology=network_topology,
            constraints={
                'max_hops': 2,
                'min_replicas': 2,
                'bandwidth_cap': '40Gb/s'
            }
        )

        self.rebalance_data(new_placement)

Conclusion: Effective distributed training requires sophisticated coordination mechanisms to maintain performance at scale while managing memory efficiently across the cluster.

Monitoring and Error Handling

Regular monitoring of key metrics helps identify potential bottlenecks and ensure optimal performance. Important metrics to track for AI storage include I/O wait times, storage throughput, data loading latency, and cache hit rates. When issues arise, this monitoring makes it much easier to identify the root cause.

To avoid common pitfalls, start with conservative settings and gradually tune based on monitoring data. Implement proper error handling and retry mechanisms, especially for distributed training scenarios. Remember that the goal is not just maximum performance but reliable, consistent performance that can be maintained throughout your training run.

Summary: An Effective Approach to High-Performance Computing Storage

Achieving optimal storage performance for large-scale ML workloads requires a comprehensive approach that extends beyond raw throughput specifications. As shown In Part 1 of our Storage Benchmarking Series, storage solutions for AI infrastructure must maintain consistent performance across varying block sizes and access patterns. Everything from the underlying architecture to the data organization can impact your storage performance and is worth benchmarking to further your team’s understanding.

Key Findings

Our optimization strategies for high-performance storage highlight several key principles:

• Effective buffering, prefetching, and cache coordination mechanisms are essential.
• Storage access patterns must be optimized for both initial data loading and checkpoint operations.
• System architecture must be complemented by optimized code implementation.

With regular performance monitoring and testing, you can validate the system capabilities of your infrastructure and deepen your knowledge of your model and infrastructure performance. Having expertise in both storage infrastructure and ML workloads enables your team to fine-tune for optimal cluster performance—which can lead to faster training time, efficient utilization of your AI resources, and greater total cost of ownership.

CoreWeave’s Approach to High-Performance Storage for AI

Our approach draws from CoreWeave's extensive experience supporting customers who regularly train large models across tens of thousands of GPUs for extended periods. At this scale, three critical factors emerge: security, performance, and stability.

• Security: CoreWeave’s suite of security capabilities and high-speed connectivity is trusted by leading AI labs and enterprises. We help to ensure a secure and dependable environment for building mission-critical AI applications for enterprises of all sizes.
• Stability: System reliability is prioritized over pure performance, as interruptions in long-running training jobs can result in substantial time and resource losses.
• Performance: Optimizing storage throughput across tens of thousands of GPUs is crucial for efficient GPU utilization and reduced operational costs. Faster training cycles can provide significant competitive advantages.

CoreWeave's experience implementing these principles has enabled customers to achieve optimal training and performance while maintaining the stability required for extended training operations. The combination of properly architected infrastructure, optimized access patterns, and performance monitoring creates an environment where ML workloads can operate at peak efficiency across thousands of NVIDIA GPUs and networking.

‍Chat with our team today to see how CoreWeave infrastructure and purpose-built storage services can meet the demanding needs of modern AI applications.