Understanding Scratch Space in HPC: A Technical Deep Dive

In High-Performance Computing (HPC), most of the attention is focused on compute: faster CPUs, cutting-edge GPUs, and ever-denser clusters. But no matter how powerful the processors are, they are ultimately limited by I/O. Data must move in and out—fast, often in parallel, and usually at scale. This is where scratch space comes into play.

Scratch space is one of the most critical, yet misunderstood, components of an HPC storage hierarchy. It’s not persistent, it’s not archival, and it’s not general-purpose—but without it, many large-scale computations would grind to a halt.

What is Scratch Space?

Scratch space is a high-speed, temporary storage area used to hold intermediate data generated during computational jobs. It acts as a working buffer for simulations, training runs, render pipelines, and other performance-intensive workloads. Once the job completes, the data in scratch space is typically discarded, either automatically or by design.

In traditional terms, it’s akin to a scratchpad memory—you use it to jot down what’s needed while thinking, then erase it when you are done.

Key characteristics include:

• High-throughput parallel access (multi-node, multi-threaded I/O)
• Short retention windows, often tied to job scheduling
• No backup or replication
• Automated or policy-driven cleanup mechanisms

The architecture may involve parallel file systems (e.g., GPFS, Lustre, BeeGFS), NVMe or SSD-backed pools, and advanced data movement logic to support bursty, high-volume I/O without polluting persistent storage systems. Though scratch space often shares the same underlying filesystem (like GPFS) as other storage tiers, it is configured and managed separately, with aggressive tuning for metadata performance, concurrency, and short-lived access patterns.

Types of Scratch Space in HPC Systems

Local Scratch (Node-Attached)

• Located on NVMe/SSD inside compute nodes
• Extremely fast, but ephemeral and isolated
• Useful for single-node workloads or caching

Shared Scratch (Parallel File System)

• Backed by high-speed shared storage
• Mounted cluster-wide for cross-node access
• Built using file systems like GPFS, Lustre, BeeGFS
• Supports massive, concurrent access patterns

Burst Buffers

• Memory or flash-based layer in front of scratch or persistent storage
• Used for write-back caching during burst I/O phases
• Often integrated via job schedulers or middleware

How Scratch Differs from Other Storage Layers

Technical Requirements for Scratch Space

To meet the demands of modern HPC workloads, scratch storage must support:

• Parallel I/O at scale: concurrent access from thousands of threads/nodes
• Low metadata latency: fast create/delete operations for small files
• Optimized striping and distribution: for large, sequential or unaligned access
• QoS enforcement and I/O throttling: to prevent job interference
• Automated data eviction: TTLs, job-end hooks, and orphan sweeps

In environments where GPFS or Lustre is used, scratch space is typically deployed as a dedicated pool or file system namespace, separate from user directories or project volumes. This allows for fine-grained tuning of block sizes, caching behavior, and failure domains.

Example Use Cases for Scratch Storage in HPC

These are just examples of how different industries use scratch storage to speed up heavy computing tasks:

• Scientific Simulations (CFD, FEA, molecular dynamics): Write massive checkpoint data and intermediate states.
• Genomic Workflows: Stage and process BAM/FASTQ files at scale.
• AI/ML Training Pipelines: Cache preprocessed datasets, shard files, or log outputs.
• Rendering and VFX: Store temporary frames, asset caches, and compositing layers.
• Climate and Weather Models: Handle timestep data, I/O burst logging, and restart files.
• Seismic Imaging: Store intermediate wavefield data and processed gathers during inversion workflows.
• Financial Risk Modeling: Handle large matrix transformations, cache intermediate risk scenarios, and stress test data.

Challenges in Managing Scratch Space

Despite its speed and purpose-specific design, scratch space presents several operational challenges:

• Capacity Sprawl: Orphaned or lingering files can consume critical space
• Performance Contention: Multi-user environments risk noisy-neighbor effects
• Monitoring Complexity: Transient data makes usage tracking more difficult
• Metadata Overhead: Millions of small files stress file system performance
• User Misuse: Using scratch as pseudo-persistent storage leads to data loss and policy violations

Solving these requires integration with job schedulers, monitoring tools, and data lifecycle automation.

Scratch Space: A Strategic Layer in the HPC Stack

Scratch space is not just a temporary disk—it is a performance-critical infrastructure layer that supports the demanding I/O characteristics of HPC workloads. When properly designed and managed, it ensures that computational resources are not bottlenecked by data movement, and that persistent storage layers remain focused on what they do best: long-term data management.

In modern architectures, scratch space deserves as much design attention as compute and memory. Its effectiveness directly impacts job throughput, resource utilization, and total cost of operation.