|By Steve Neuner, Dan Higgins||
|May 18, 2004 12:00 AM EDT||
Previous notions of limited scalability of Linux were abruptly changed last year by the introduction of the SGI Altix server, which scaled up to 64 processors within a single system image (SSI). Today, large-scale Linux servers with hundreds of processors are being deployed by a variety of businesses, universities, research centers, and governments around the world. NASA Ames Research Center, for example, continues to push the limits even further with their 512-processor system running a single instance of the Linux kernel.
This article examines the challenges in enabling large numbers of processors to work efficiently together to better support Linux system configurations for High- Performance Computing (HPC) environments. We will explain what scaling is, the importance of good hardware design, and the kernel changes that make scaling Linux on systems up to 256 processors and beyond possible. Finally, we will show examples of how these highly scalable Linux systems are being used to solve complex real-world problems more efficiently.
Scaling Within HPC EnvironmentsFirst, let's examine the issues behind system scalability. The term scaling refers to the ability to add more hardware resources, such as processors or memory, to improve the capacity and performance of a system. There are different strategies used for scaling systems depending on the workload requirements. Enterprise business server workloads, for example, often consist of many individual, unrelated tasks that are typically deployed on systems that are smaller in nature and networked together. HPC workloads, on the other hand, are composed of scientific programs that require a high degree of complex processing, process large amounts of data, and have widely fluctuating resource requirements. Because of their demanding resource requirements, HPC programs are written and parallelized to break complex problems down to enable them to leverage system resources in parallel.
One approach used to solve HPC problems is horizontal scaling. With this approach, a program's threads run across a "cluster" of separate systems, and these threads communicate and exchange data over the network. This strategy can be used for workloads that are embarrassingly parallel, where little communication is required between program threads as they perform their computations. However, when program threads need to interact while working on a common set of data, vertical scaling provides a more efficient and better approach. With vertical scaling, threads run on a large number of CPUs all within one system, enabling processors to communicate more efficiently and to also operate upon and exchange data using global shared memory. Adding more processors to the system enables more threads to run simultaneously, thereby enabling more resources to be applied and shared to solve a problem. Vertical scaling also provides an ideal environment for using an HPC system as a central server to dynamically run different HPC programs at the same time when any one program either doesn't actually need all of the system processors or has its own scaling limitations. Whether greater processing capability for a single HPC program is required, or increasing throughput for several different HPC programs running at once, a properly designed vertically scaled system provides a flexible and superior environment for both the most demanding and the widest range of HPC applications.
Hardware Design and ScalabilityPerfect scaling occurs when the number of processors added improves the workload throughput by the same factor. For instance, a four-processor system should theoretically improve processing power fourfold compared to a single processor system. In a multiprocessor system, it is critical to minimize the overhead involved with coordinating among multiple processors and utilizing shared resources. We say, "the system is scaling linearly at 90 percent up to 4 processors" if adding a second processor improves system performance by 1.8X, adding a third processor yields a 2.7X improvement, and adding a fourth processor yields an improvement of 3.6X over a single CPU. As more processors are added to a system, often a point is reached where performance no longer improves or even decreases due to hardware, kernel, or application software limitations. The goal is to improve performance by enabling multiple CPUs to scale as close to perfect as possible, and to the highest possible numbers of CPUs.
One of the keys to obtaining maximum performance is a fast system bus with high bandwidth. The extreme processing power provided by hundreds of high-performance CPUs requires multiple fast paths for handling data between CPUs, caches, memory, and I/O. The system bus found on symmetric multiprocessing systems can quickly become a bottleneck since all traffic from the CPUs uses a single, common bus to access and transfer data. Much higher system performance is available using a non-uniform memory access (NUMA) architecture since CPU accesses to memory within the same node will distribute and reduce the load on the system interconnect (see Figure 1).
A well-designed NUMA system will carefully account for the CPU bus transfer speeds, number of CPUs on any given bus, memory transfer speeds, multiple paths, and other factors to ensure that maximum overall bandwidth can be delivered throughout the system. Drawing an imaginary line through the middle of a system to examine its maximum capacity for transferring data between two halves is called bisectional bandwidth. Figure 2 shows the system bus interconnect for an SGI Altix system designed for overall maximum bisectional bandwidth and performance. In this diagram, each C-brick is a rack-mountable module containing four CPUs and each R-brick is an SGI NUMAlink module used to connect together and make a 128p SGI Altix system.
A computer architecture that is well balanced and built for maximum performance is essential to achieving good system scalability. If the hardware doesn't scale, neither will the Linux kernel or the user's application.
Linux Kernel ScalabilityLinux was originally designed for smaller systems. Extending Linux to scale well on large systems involves extending various sizes and tables managed by the kernel, and then optimizing the performance for high-end technical computing. Thanks to the solid design and wide community support, Linux has adapted well to large systems.
SGI kernel engineers found that while they were clearly the first to run Linux on large system configurations of this kind, the Linux community had already done an excellent job reworking and addressing many of the issues related to Linux scalability. The types of changes made by SGI and others within the community include extending resource counters sizes, extending bit-mask sizes, and fixing commands and tools to support more than double-digit CPU numbers. Other changes included adding NUMA tool commands to help manage larger memory sizes more efficiently, increasing the limit on open file descriptors and on file sizes, and reducing boot time console messages generated by each processor, since administrating and troubleshooting would otherwise be unmanageable on systems with large CPU counts.
Once the kernel was modified to accommodate the resources of a larger system, SGI engineers focused on getting Linux to scale and perform well. One way to find scaling problems for a 256-processor system is to turn up the stress knobs while using a much larger configuration, such as a 512-processor system. Problems that otherwise would be difficult to pinpoint become obvious. Developing and testing on these larger configurations enabled the SGI engineering team to find and fix many problems that affect all multiprocessor systems of all sizes. SGI kernel engineers used several large configurations in this manner to run a variety of different HPC applications, benchmarks, and custom tests to identify and diagnose Linux scaling problems. Figure 3 shows an early 512 processor SGI Altix system, ascender, which was used by SGI kernel engineers to find and fix scaling problems.
Such testing uncovered a number of areas to change for improving scalability. For example, some system-wide kernel variables were converted to per-processor variables. This reduces memory contention on shared data such as global kernel performance statistics, since this data could be maintained separately, then combined only when needed for reporting purposes. Other scaling improvements included finding and eliminating high-contention spinlocks, reducing spinlock contention in timer routines, optimizing process scheduling algorithms, changes in the buffer cache to use per-node data structures, improved translation lookaside buffer algorithms, improved parallelism of page fault and out-of-memory handling, and identifying and removing hot cache lines due to false sharing.
Bringing It All TogetherA well-designed hardware system combined with the Linux optimizations described here enables hundreds of processors within a system to access, use, and manipulate shared resources in the most efficient manner possible, enabling users' HPC programs to fully exploit the available system resources to do real work. The following three examples demonstrate the dramatic scaling and performance improvements being achieved with Linux on systems with processor counts of 128, 256, and larger.
The first example (see Figure 4) shows how adding processors to a system can dramatically reduce the elapsed time for the bioinformatics HPC application HTC-BLAST (High Throughput Computing - Basic Logical Alignment Search Tool) to process 10,000 queries with 4,111,677 total letters on a human genome database with 545 sequences and 2,866,452,029 total letters. In particular, notice that a system with 128 processors ran 1.77X faster than a system with 64 processors.
The next example (see Figure 5) shows the scaling and performance improvements achieved using a computation fluid dynamics application on an automobile external flow problem with a model size of 100 million cells. In this case the total elapsed time continues to decrease as the system configuration is extended from 64 to 256 processors.
Finally, the third example (see Figure 6) shows scaling results for an OpenMP code called Cart3D, developed and used extensively by the NASA Ames Research Center to study flows for the space shuttle. NASA Ames Research Center, known for pushing the limits of computing in pursuit of fundamental science, achieved almost 90% scaling efficiency while running this HPC code on a 512-processor SGI Altix system. SGI and NASA engineers collaborated to identify and fix many Linux scaling issues to achieve a dramatic new breakthrough on system scalability with Linux. The NASA Ames Research Center's system used for this work is shown in Figure 7.
SummaryThe performance and capabilities of Linux for server environments have improved dramatically in just the last year. Scientists and others are now routinely using single-system Linux configurations with hundreds of processors to solve complex problems faster and with greater ease than had been thought possible. Testing and developing on these large configurations have proven invaluable for improving the reliability and performance of Linux on configurations of all sizes. The synergy of these scaling improvements combined with the open development model has enabled the continued advancement of Linux to become the superior operating system choice for delivering performance and stability in all environments.
Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, will discuss how research has demonstrated the value of Machine Learning in delivering next generation analytics to im...
Apr. 29, 2016 03:45 PM EDT Reads: 1,576
This is not a small hotel event. It is also not a big vendor party where politicians and entertainers are more important than real content. This is Cloud Expo, the world's longest-running conference and exhibition focused on Cloud Computing and all that it entails. If you want serious presentations and valuable insight about Cloud Computing for three straight days, then register now for Cloud Expo.
Apr. 29, 2016 03:30 PM EDT Reads: 1,637
As you respond to increasing requests for new analytics, you need fast and flexible technology in your arsenal so that you can deploy the right workload to the right platform for the need at hand. Do you need self-service and fast time to value? Do you have data and application control and privacy needs, along with strict SLAs to meet? IBM dashDB™ is data warehouse technology powered by in-memory computing and in-database analytics that are designed for fast results, scalability and more.
Apr. 29, 2016 03:15 PM EDT Reads: 1,513
You think you know what’s in your data. But do you? Most organizations are now aware of the business intelligence represented by their data. Data science stands to take this to a level you never thought of – literally. The techniques of data science, when used with the capabilities of Big Data technologies, can make connections you had not yet imagined, helping you discover new insights and ask new questions of your data. In his session at @ThingsExpo, Sarbjit Sarkaria, data science team lead ...
Apr. 29, 2016 03:07 PM EDT Reads: 114
SYS-CON Events announced today that SoftLayer, an IBM Company, has been named “Gold Sponsor” of SYS-CON's 18th Cloud Expo, which will take place on June 7-9, 2016, at the Javits Center in New York, New York. SoftLayer, an IBM Company, provides cloud infrastructure as a service from a growing number of data centers and network points of presence around the world. SoftLayer’s customers range from Web startups to global enterprises.
Apr. 29, 2016 03:00 PM EDT Reads: 803
So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, will provide tips on how to be successful in large scale machine lear...
Apr. 29, 2016 02:45 PM EDT Reads: 783
Up until last year, enterprises that were looking into cloud services usually undertook a long-term pilot with one of the large cloud providers, running test and dev workloads in the cloud. With cloud’s transition to mainstream adoption in 2015, and with enterprises migrating more and more workloads into the cloud and in between public and private environments, the single-provider approach must be revisited. In his session at 18th Cloud Expo, Yoav Mor, multi-cloud solution evangelist at Cloudy...
Apr. 29, 2016 02:30 PM EDT Reads: 1,377
IoT device adoption is growing at staggering rates, and with it comes opportunity for developers to meet consumer demand for an ever more connected world. Wireless communication is the key part of the encompassing components of any IoT device. Wireless connectivity enhances the device utility at the expense of ease of use and deployment challenges. Since connectivity is fundamental for IoT device development, engineers must understand how to overcome the hurdles inherent in incorporating multipl...
Apr. 29, 2016 02:30 PM EDT Reads: 1,387
We’ve worked with dozens of early adopters across numerous industries and will debunk common misperceptions, which starts with understanding that many of the connected products we’ll use over the next 5 years are already products, they’re just not yet connected. With an IoT product, time-in-market provides much more essential feedback than ever before. Innovation comes from what you do with the data that the connected product provides in order to enhance the customer experience and optimize busi...
Apr. 29, 2016 02:00 PM EDT Reads: 813
The IETF draft standard for M2M certificates is a security solution specifically designed for the demanding needs of IoT/M2M applications. In his session at @ThingsExpo, Brian Romansky, VP of Strategic Technology at TrustPoint Innovation, will explain how M2M certificates can efficiently enable confidentiality, integrity, and authenticity on highly constrained devices.
Apr. 29, 2016 02:00 PM EDT Reads: 962
The paradigm has shifted. A Gartner survey shows that 43% of organizations are using or plan to implement the Internet of Things in 2016. However, not just a handful of companies are still using the old-style ad-hoc trial-and-error ways, unaware of the critical barriers, paint points, traps, and hidden roadblocks. How can you become a winner? In his session at @ThingsExpo, Tony Shan will present a methodical approach to guide the holistic adoption and enablement of IoT implementations. This ov...
Apr. 29, 2016 02:00 PM EDT Reads: 1,521
In his session at 18th Cloud Expo, Sagi Brody, Chief Technology Officer at Webair Internet Development Inc., will focus on real world deployments of DDoS mitigation strategies in every layer of the network. He will give an overview of methods to prevent these attacks and best practices on how to provide protection in complex cloud platforms. He will also outline what we have found in our experience managing and running thousands of Linux and Unix managed service platforms and what specifically c...
Apr. 29, 2016 01:45 PM EDT Reads: 1,049
SYS-CON Events announced today that Peak 10, Inc., a national IT infrastructure and cloud services provider, will exhibit at SYS-CON's 18th International Cloud Expo®, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. Peak 10 provides reliable, tailored data center and network services, cloud and managed services. Its solutions are designed to scale and adapt to customers’ changing business needs, enabling them to lower costs, improve performance and focus inter...
Apr. 29, 2016 01:30 PM EDT Reads: 791
Artificial Intelligence has the potential to massively disrupt IoT. In his session at 18th Cloud Expo, AJ Abdallat, CEO of Beyond AI, will discuss what the five main drivers are in Artificial Intelligence that could shape the future of the Internet of Things. AJ Abdallat is CEO of Beyond AI. He has over 20 years of management experience in the fields of artificial intelligence, sensors, instruments, devices and software for telecommunications, life sciences, environmental monitoring, process...
Apr. 29, 2016 01:30 PM EDT Reads: 763
In the world of DevOps there are ‘known good practices’ – aka ‘patterns’ – and ‘known bad practices’ – aka ‘anti-patterns.' Many of these patterns and anti-patterns have been developed from real world experience, especially by the early adopters of DevOps theory; but many are more feasible in theory than in practice, especially for more recent entrants to the DevOps scene. In this power panel at @DevOpsSummit at 18th Cloud Expo, moderated by DevOps Conference Chair Andi Mann, panelists will dis...
Apr. 29, 2016 01:30 PM EDT Reads: 456