Optimizing Data Center TCO With CXL And Compression

In the ever-evolving landscape of data centers, Total Cost of Ownership (TCO) remains a critical metric. It encompasses all costs associated with data center infrastructure throughout its lifecycle, including initial purchase, installation, utilization, maintenance, energy consumption, and eventual replacement. By understanding and optimizing TCO, hyperscalers can make informed decisions that enhance data center operations and minimize overall expenses.

A significant portion of the data center’s costs is attributed to compute, networking, memory and storage infrastructure. The focus in this blog will primarily be on the memory and storage infrastructure. Different types of memory and storage tiers are available in the data center which are optimized to meet the needs of various application workloads. Applications are optimized to choose the right tier depending on how frequently the data is accessed along with the performance requirements of the associated workload. For instance, “hot” data, which is accessed frequently, resides in faster, more expensive tiers like SRAM and DRAM. In contrast, “warm” and “cold” data, accessed less frequently, are stored in slower tiers like SSDs and HDDs. The cost per gigabyte decreases with lower performance tiers. High-speed trading applications, for example, require high-performance memory due to their latency sensitivity, while media archives or data backups can be stored in cold tiers with less stringent latency requirements.

Given the promise of significant TCO contributions, the adoption of Compute Express Link (CXL) continues to grow with wide industry support. It leverages the broadly adopted PCI Express interconnect for its physical link layer​ and delivers memory cache coherency allowing memory resources to be pooled and shared between computing devices​. CXL technology addresses a significant challenge in data centers: stranded memory. Stranded memory refers to underutilized memory resources, which contribute to poor memory utilization and higher TCO. As application workloads become more diverse, the need for disaggregated infrastructure grows. CXL enables memory pooling, allowing memory to be disaggregated and allocated to servers on-demand. This results in effective memory utilization, eliminating stranded memory, and ultimately improving TCO.

Since its introduction, CXL has seen significant improvements, including enhancements in fabric connectivity, memory RAS (Reliability, Availability, and Serviceability), and security. The latest version, CXL 3.1, introduces features such as fabric decode and port-based routing, direct peer-to-peer memory support, and a Trusted Execution Environment (TEE) security protocol. These advancements make CXL a robust solution for disaggregation, pooling, and distributed processing with high reliability and security.

Memory compression is a technique that reduces the amount of data stored in memory by encoding it in a more compact form. This not only increases the effective memory capacity but also reduces the overall memory cost. By integrating compression with CXL, we can create a new memory tier that balances performance and cost. The benefits of compression in the memory tier include:

1. Cost Efficiency: Compression can significantly reduce memory costs. For instance, a 2:1 compression ratio can halve the media cost, making it a viable solution for creating a memory tier with a lower $/GB.
2. Improved Utilization: By compressing cold data (data that hasn’t been used recently), memory resources can be managed better. Cold data typically has good compressibility and requires minimal bandwidth, making it ideal for a compressed memory tier.
3. Enhanced Performance: Integrating compression with CXL allows for efficient data movement, reducing system latency and power consumption. This is particularly beneficial for AI and machine learning workloads, which demand high-performance memory solutions.

The DenseMem compression solution from ZeroPoint Technologies integrated with Rambus CXL controller IP is an example of an implementation that can deliver a new memory tier that balances performance and cost. Hyperscalers have already recognized the potential of CXL and compression. A base specification for a new category of CXL device was introduced, focusing on cost and efficiency. This includes support for DDR4, inline memory compression, and a cache for decompressed pages, all aimed at reducing overall memory costs.

The combination of CXL and memory compression offers a promising solution to address the challenges of high memory costs and inefficient utilization in data centers. By creating a new memory tier that leverages these technologies, system designers now have the option to optimize the infrastructure further to address the needs of evolving use cases. Finally, this new memory tier can achieve significant cost savings and performance improvements, ultimately improving the TCO.

Links:

• ZeroPoint DenseMem CXL Memory Expansion
• Rambus CXL 3.1 Controller IP