Steve Scargall of MemVerge, discusses how compute express link (CXL) addresses memory challenges in AI/ML, ensuring optimal performance for complex and data-intensive applications.Â
Artificial intelligence (AI) and machine learning (ML) applications pose significant challenges to memory management and performance optimization due to their complexity and data intensity. In this article, we will explore how compute express link (CXL), a new industry-standard interconnect, can help solve some of the common memory problems faced by AI/ML applications and boost their performance. CXL enables memory coherency and expansion for AI/ML applications by allowing the CPU and the devices to share the same view of the memory and access it with low latency and high bandwidth. CXL also enables resource sharing and dynamic load balancing among heterogeneous devices. We will discuss how CXL can help solve out-of-memory errors, spill to disk, and data/compute skewing for AI/ML applications and provide some examples of products and solutions that support CXL.
Artificial intelligence (AI) and machine learning (ML) are transforming various industries and domains with unprecedented capabilities and applications. However, as AI/ML models become larger, more complex, and data-intensive, they pose significant challenges for memory management and performance optimization. We have seen what CXL is and how it works in Part 1 of this series. Let’s explore how compute express linkOpens a new window (CXL) can help solve some common memory problems AI/ML applications face and boost their performance.
Solving Out Memory Errors
One of the common memory problems faced by AI/ML engineers is “out of memory†(OOM) errors. OOM errors occur when the application tries to allocate more memory than what is available on the system. This can happen for various reasons, such as insufficient memory capacity, fragmentation, leaks, etc. OOM errors can cause the application to crash or be killed by the Kernel’s OOM Killer. Work has to be restarted, sharded across multiple compute nodes, or performed in batches.
CXL can help solve OOM errors by enabling memory expansion for AI/ML applications. With CXL, the application can access memory pools without any software modifications. CXL memory expansion devices augment the main memory (DRAM) to increase capacity and bandwidth. The increased memory capacity available for AI/ML applications can reduce or eliminate the chances of OOM errors. For example, CXL memory expansion devices that deliver 64GiB up to multi-terabyte capacities from many vendors are emerging. CXL memory expansion devices can be installed in the server as a local resource or shared amongst multiple servers called memory pools that consist of Just a Bunch of Memory (JBOM) or intelligent memory appliances. Coupled with CXL switches, memory fabrics can be created that allow almost limitless memory capacities.
See More: 4 steps to troubleshooting (almost) any IT issue
Reducing Spilling to Disk
Another common memory problem AI/ML applications face is spill to disk. Spill to disk occurs when the application runs out of memory and has to move some of its data from memory to disk. This can happen for various reasons, such as insufficient memory capacity, memory pressure, contention, etc. Spill to disk can significantly degrade application performance due to the high latency and low bandwidth of disk access relative to main memory.
CXL can help solve spill to disk by enabling memory coherency and expansionOpens a new window . With CXL, the CPU, memory expanders, and accelerators can share the same coherent view of the memory and access it without any software intervention or synchronization. This can reduce the need for data movement between CPU and accelerators and improve data locality and performance. CXL also expands memory by allowing the CPU to access larger memory pools on attached devices with low latency and high bandwidth. This can increase the available memory capacity and performance for AI/ML applications and reduce the chances of the spill to disk.
See More: SSD vs. HDD: Choosing Your Storage Solution
How CXL Can Solve Data/Compute Skewing
A third common memory problem AI/ML applications face is data or compute skewing. Data or compute skewing occurs when the distribution of data or compute resources among heterogeneous devices is uneven or imbalanced. This can happen for various reasons, such as data partitioning, load balancing, resource allocation, etc. Data or compute skewing can cause performance degradation for AI/ML applications due to resource underutilization or contention.
CXL can help solve data and compute skewing by enabling resource sharing and dynamic load balancing among heterogeneous devices. With pools of CXL devices available to multiple hosts, the resources can be configured to the compute node(s) as needed. This paradigm shift allows memory and computing to be composed to fit the application’s needs. In contrast, today, the application and data must be carefully managed and partitioned to fit the available hardware resources. With intelligent resource management software, sharing and dynamic load balancing among heterogeneous CXL devices based on the compute node workload characteristics and requirements can perform real-time resource [de]allocation. For example, memory can be allocated to one or more hosts during high memory demand phases of computation and released back to the memory pool when the demand for memory reduces. Memory can be re-assigned between hosts as needed. This can improve data locality and performance for AI/ML applications, ultimately reducing the time to insight.
Harnessing the Potential of CXL’s Memory Solutions
CXL can help solve some common memory problems AI/ML applications face and boost their performance. CXL enables memory coherency and expansion for AI/ML applications by allowing the CPU and the devices to share the same view of the memory and access it with low latency and high bandwidth. With intelligent resource management and allocation software, CXL also enables resource sharing and dynamic load balancing among heterogeneous devices by allowing them to access each other’s memory or cache without any software overhead or data movement. CXL can help solve OOM errors, spill to disk, and data/compute skewing for AI/ML applications and improve their performance and efficiency.
CXL is a game-changer for AI/ML applications and is expected to become widely adopted and standardized shortly if you want to learn more about CXL and how it can boost your AI/ML performance.Â
What steps have you taken to address memory challenges for AI/ML applications? Let us know on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!
Image Source: Shutterstock