While solid state drives (SSDs) offer superior read speeds, their design introduces specific performance caveats. First, SSDs store data in blocks (typically $$256$$ KB or larger) that must be read, erased, and rewritten as a whole, resulting in poor performance for bit-wise random writes. Although modern SSD controllers and firmware have developed algorithms to mitigate this, write speeds can still be much slower, in particular for QLC (quad level cell) SSDs. To optimize write speeds, it is necessary to maintain a queue of operations, prefer reads, and write in large blocks. Second, flash memory cells degrade over time, often wearing out after a few thousand write cycles. While wear-leveling algorithms help spread this degradation, SSDs are not recommended for high-frequency write tasks like swapping files or continuously aggregating large log files. Finally, the massive increase in bandwidth has forced computer designers to attach SSDs directly to the PCIe bus. The drives capable of handling this, referred to as NVMe (Non Volatile Memory enhanced), can use up to $$4$$ PCIe lanes, achieving up to $$8$$ GB/s on PCIe $$4.0$$.

Performance Caveats of Solid State Drives (SSDs)

Solid state drives (SSDs) use persistent flash memory to store information, offering significantly faster access times compared to traditional hard drives. Modern SSDs can perform 100,000 to 500,000 input/output operations per second (IOPs)—up to 3 orders of magnitude faster than HDDs—and achieve bandwidths of 1 to 3 GB/s. High-performance NVMe (Non-Volatile Memory Express) SSDs connect directly to the PCIe bus, leveraging up to 4 lanes to reach speeds up to 8 GB/s on PCIe 4.0. This massive throughput makes SSDs highly desirable for rapidly loading large deep learning datasets.

Claude

Google

Durable storage, such as magnetic hard disk drives (HDDs) or solid-state drives (SSDs), provides long-term retention of information. In deep learning workflows, these components are typically connected via the PCIe bus and are responsible for the efficient transfer of massive training datasets to the system, as well as for securely storing intermediate model checkpoints.

Durable Storage in Deep Learning

Dive into Deep Learning

Like RAM, durable storage devices are primarily characterized by their bandwidth and latency. In deep learning workflows, the differences in these performance metrics across various storage technologies can be extreme, significantly impacting the speed at which large datasets can be loaded into the system for processing.

Storage Bandwidth and Latency in Deep Learning

Solid State Drives (SSDs) in Deep Learning

Cloud storage provides a highly configurable and dynamic approach to data retention for virtual machines. Users can dynamically adjust the assignment of storage based on both total capacity and desired speed. In deep learning workflows, practitioners are encouraged to actively increase the provisioned input/output operations per second (IOPs) when encountering high latency, such as during training phases that require processing numerous small data records.

Cloud Storage in Deep Learning

Hard disk drives (HDDs) utilize spinning platters and magnetic heads to read and write data. While HDDs are relatively inexpensive and offer high capacities—with high-end models holding up to $$16$$ TB—they suffer from typically catastrophic failure modes and high read latencies. Because physical platters must rotate into position, accessing data can take over $$8$$ ms, limiting HDDs to approximately $$100$$ input/output operations per second (IOPs). Spin speeds are physically limited by centrifugal force; spinning much faster than their typical $$7,200$$ RPM would shatter the platters. Furthermore, increasing bandwidth is difficult because the bit rate only scales with the square root of the information density, keeping bandwidth restricted to roughly $$100$$–$$200$$ MB/s. Due to these performance bottlenecks, HDDs are primarily relegated to archival purposes and low-grade storage for massive datasets in deep learning.

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