Intel Charges Spark Workloads with Optane Persistent Memory

By Alex Woodie

July 30, 2019

Intel didn’t wow chip lovers earlier this year with the launch of its 2nd Generation Intel Xeon Scalable processors “Cascade Lake” processors, which are based on the same 14nm process as the first generation processors. But the launch also included the delivery of Optane Data Center Persistent Memory Module (DCPMM), which is poised to deliver big benefits for SQL and machine learning workloads on Apache Spark and other frameworks.

Optane is Intel‘s latest storage innovation that blends the characteristics of fast but volatile RAM and slower but persistent NAND storage technology. Originally based on the 3D Xpoint technology that it started co-developing with Micron years ago, the storage-class memory technology was designed to provide a major boost in the ability of users to work with large data sets by providing the speed of DRAM but the capacity and persistence of NAND.

Intel already shipped an Optane product in the form of NVMe drive, and now it’s coming to market in the guise of Optane DCPMMs. Delivered as standard DIMMs, Optane DCPMMs plug right into the PCIe bus on industry-standard X86 servers. Those systems will, however, need to be running Cascade Lake processors, while the NVMe format was more flexible in system configurations. However, what the DCPMM lacks in flexibility it should make up in capability.

Intel is shipping DCPMMs in three sizes: 128GB, 256GB, and 512GB. Each DCPMM requires its own memory channel, and customers can load up to six DCPMM DIMMs in single socket. Users can co-locate DCPMMs next to DRAM, but they cannot use multiple sizes of DCPMMs.

This gives customers with a two-socket server the capability to have up to 6TB of memory per server, according Intel engineer Piotr Balcer, who spoke at Databricks’ Spark + AI Summit 2019 recently. “Quite a lot of space for your whole data,” he said.

With multiple servers, DCPMM enables customers to store up to 1PB of data in 1U of a rack, Intel said.

Speeding Data with Optane

There are two modes supported with Optane DCPMM: App Direct mode and Memory Mode (there’s also Storage Over App Direct Mode).

Users who want to take advantage of Optane’s data persistence capabilities will need to choose App Direct mode, since the data is wiped clean during power shutdowns in Memory Mode, which is how traditional DRAM works. (But because DCPMMs are still cheaper than traditional DRAM DIMMs, Optane retains an advantage.)

Balcer and his Intel colleague Cheng Xu demonstrated how Spark users can get a performance boost during their Spark + AI Summit session, titled “Accelerate Your Apache Spark with Intel Optane DC Persistent Memory.”

“Persistent memory is exposed to the application through the file system,” Balcer said. “It uses the same normal interface as storage.” Currently DCPMM supports XFS, EXT4, and NTFS file systems, using those file systems’ block storage system calls to read and write data.

“It behaves like DRAM for access, so you can use normal load semantics, which means we don’t have to go through the kernel,” he said. “We don’t have to have that control point.”

To enable applications to directly access data and use normal load storage instructions, Intel developed something called DAX, which stands for direct access, Balcer said.

“Which means it allows the application to mount the persistent memory into the other space of the application, and bypass the page cache, because the page cache is what you traditionally will use when you use memory mounts,” he said. “Because we now have very, very fast memory, we don’t really need the page cache to amortize the block storage.”

Using the DAX layer, DCPMM enables users to mount persistent memory into the address space of the application and then use the load store instructions of the CPU, which is the fastest data path the application can take, Balcer said.

“What this ultimately means,” he continued, “is that the application can now store data persistently on storage yielding the load store instructions of the CPU, and that was never possible before. So there’s nothing in between the application and the storage. There’s no software. There’s no firmware. Well, there is firmware, but there’s nothing in the kernel space that interferes with the application performance.”

Spark on Optane

The performance benefits of DCPMM are directly applicable to Spark SQL and machine learning workloads that are either memory-bound or are burdened by large amounts of I/O, the Intel engineers told the Spark + AI audience.

In App Direct Mode, DCPMM has the potential to move data at multiple tens of gigabytes per second with nanosecond latencies, compared to single-digit GB/s on throughput with microsecond latencies with fast NAND-based solid state disks, according to Lenovo’s handbook on DCPMM.

However, achieving those rates requires users to specially configure their DCPMM setup, Lenovo states. “If an application hasn’t been modified to support App Direct Mode, it can utilize DCPMM in Storage over App Direct Mode operation, which is a more conventional setup using a supported DAX model in the operating system,” the vendor writes.

Intel has addressed this requirement by developing special software that allows Spark users to take full advantage of the DCPMM capabilities, without modifying their Spark machine learning or SQL applications.

Intel’s Spark on DCPMM stack consists of several layers, including a DAX file system interface discussed above, Intel’s native DCPMM library dubbed VMEMCACHE, and OAP, or the Open Analytics Packager.

The Scala-based OAP contains several elements that make it easy for Spark users to take advantage of DCPMM with their SQL and machine learning workloads, According to Xu.

“Today Spark is running very fast and very easy for the user to use. But sometimes a customer may be facing a memory issue,” Xu said. “We hear a lot of customer complaining about memory usage, so that sometimes they try to config the memory for specific workload, but when they try to run another workload, they run into other issues.”

Optimizing Spark’s usage of memory is one of the goals of OAP, which is a free and open source piece of software that users can obtain at Github. OAP levaerges DCPMM to bring three key capabilities to Spark users, including a front-end I/O cache, a cache-aware scheduler, and self-management of off-heap memory.

The I/O cache will be especially useful for Spark SQL and machine learning users who are pulling data from slower data stores, including on-premise hard disk drives and also from remote BLOB stores, such as Azure ALDS or Amazon S3, Xu said.

“For example, you have a table ABC, but for your workload, you just access the first column A,” Xu said. “In our implementation, we just cache column A because column A is hotter data compared to the rest of the columns.”

The I/O cache will also help machine learning use cases on Spark, Xu said, particularly for algorithms with an interactive nature, such as Kmeans. “Now we have a very large capacity of memory, so you can put the entire data set into the Optane persistent memory, so you can achieve even better performance than the previous tiered storage design,” he said.

OAP’s cache-aware scheduler will also boost performance for Spark users by optimizing workloads according to data locality, Xu said. The cache-aware scheduler is based on Apache Spark version 2.0 APIs, he added. Lastly, better management of off-heap memory will also boost Spark application, Xu said.

You can access the Intel employees’ recorded Spark + AI Summit session here.

This article originally appeared at sister publication Datanami.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire