MEMCON 2024: Insights into CXL, HBM, GenAI, and More

By Doug Eadline

March 6, 2024

As the world focuses on GenAI, there is still plenty of other computing going on worldwide. A common denominator is the expanding role of memory technology in GenAI, HPC, and beyond. And, like all other technologies, new and exciting changes are occurring with memory design and implementations that will play out in everything from cell phones to Exascale systems.

To help keep track of all the new developments, vendors and users are invited to MEMCON 2024, which will be held from 26 to 27 March 2024 at the Computer History Museum in Silicon Valley, CA. 

As an introduction to some of the issues and technologies that will be discussed at MEMCON, HPCwire had the opportunity to ask Featured Speaker Tejas Chopra, Senior Engineer at Netflix, some important questions about memory technology in 2024.

MEMCON 2024 Featured Speaker Tejas Chopra, Sr. Engineer at Netflix

HPCWire: With CXL 2.0 Memory Pooling hitting the market, what application areas can you take advantage of this capability?

Tejas Chopra: Compute Express Link (CXL) 2.0 significantly advances memory pooling technology. It enables high-speed, efficient sharing of memory resources between CPUs and devices like GPUs, FPGAs, and accelerators. This capability opens up numerous application areas:

  • High-Performance Computing (HPC): In HPC, where large datasets are processed, CXL 2.0 can help reduce the data transfer latency between CPUs and accelerators, improving the performance of compute-intensive applications.
  • Machine Learning and AI: Machine learning models, especially deep learning, require significant memory resources. CXL 2.0 can help facilitate faster training of these models by allowing efficient sharing of memory resources between CPUs and GPUs.
  • Cloud Computing and Virtualization: In cloud and virtualized environments, CXL 2.0 can more efficiently utilize memory resources, allowing for better performance and cost-effectiveness.
  • Data Analytics: For real-time data analytics, where speed is crucial, CXL 2.0 can help reduce latency and increase throughput.

HPCWire: What software management tools will be required to utilize CXL fully in a data center setting, and who is creating them?

Tejas Chopra: To fully utilize CXL in a data center, we would need software management tools that can handle resource allocation, monitor performance, and ensure security and reliability. These tools would need to support hot-plugging, error handling, and power management, among other things.

 Several companies and organizations are working on such tools. Intel, for instance, has been a key player in the development of CXL and is likely to provide software support for it. Other major hardware vendors like AMD, ARM, and IBM are also part of the CXL consortium and are expected to contribute to the software ecosystem. Open-source projects like Redfish (for hardware management) and OpenStack (for cloud computing) might also play a role in providing software tools for managing CXL.

HPCWire: Many memory technologies are available to designers for specific uses, including DDR, LPDDR, GDDR, HBM, and MCR-DIMMs. Are there other memory technologies on the horizon that help solve particular problems?

Tejas Chopra: Indeed, several emerging memory technologies aim to solve specific problems:

  • Storage Class Memory (SCM): SCM, like Intel’s Optane, blurs the line between memory and storage. It provides near-DRAM performance with the persistence of traditional storage, which can benefit certain applications like big data analytics and in-memory databases.
  • Resistive Random Access Memory (ReRAM): ReRAM is a type of non-volatile memory that can retain data without power. It can potentially provide faster and more durable storage than traditional flash storage.
  • Magnetoresistive Random Access Memory (MRAM): MRAM is another type of non-volatile memory that offers high performance and endurance. It could be used in applications where data retention, speed, and endurance are critical.

HPCWire: In HPC, some HBM-augmented systems will not add standard DDR memory because it may slow down applications. Do you see this as a trend: HBM-only processors with fully Integrated HBM memory subsystems?

Tejas Chopra: High Bandwidth Memory (HBM) provides significantly higher bandwidth than standard DDR memory, which can benefit applications that need to process large amounts of data quickly, such as in HPC or graphics rendering. However, HBM is more expensive and consumes more power than DDR memory.

While there might be a trend towards HBM-only systems in specific niches where performance is paramount, it’s unlikely to become a broad trend in the near future due to cost and power consumption considerations. Instead, we might see more systems that use a mix of HBM and DDR memory, leveraging the strengths of each according to the needs of specific applications.

HPCWire: Do you think the surge in GenAI will drive new memory technologies in a specific direction? Or will it help accelerate an existing memory technology?

Tejas Chopra: The rise of General Artificial Intelligence (GenAI) is likely to drive both the development of new memory technologies and the acceleration of existing ones. GenAI requires processing and analyzing vast amounts of data, which places significant demands on memory regarding capacity, speed, and energy efficiency.

New memory technologies like SCM, ReRAM, and MRAM could meet these demands. At the same time, the need for high-speed, high-capacity memory could also drive further advancements in existing technologies like HBM and GDDR.

Moreover, the need for memory that can support the training of AI models could also drive the development of new memory architectures. For instance, in-memory computing, where computation is performed directly within memory cells, could potentially provide significant speedups for AI workloads

Thanks to Tejas Chopra for the insightful answers. Be sure to check out MEMCON 2024 for the full program and use the code HPCWIRE15 when registering to receive a 15% discount.




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