Benchmarking Your Cloud

By John E. West

July 16, 2009

It was inevitable that with all the hype and marketing dollars directed at cloud computing these days that someone would eventually start trying to use them for real work. Of course, this puts a nasty wrinkle into marketing plans because once people starting using them for real work, then there are actual performance results. The results themselves aren’t too troubling because they are usually point cases, and negative messages are easily explained away by calling on the vagaries of a particular software stack and the giving away of snazzy memory sticks. But then the results lead the engineering-minded to wonder whether all of the available cloud computing alternatives behave in the same way, and if not which of the them might be best suited for a particular task. This leads to standardized testing and then, before you know it, we have full-fledged benchmarking on our hands.

Not great for marketing departments, but wonderful for customers. And the good news for customers — and potential customers — of cloud computing is that the community is starting to think seriously about benchmarking the performance of clouds.

There is a long history in benchmarking computer hardware or software components tests that isolate, as much as possible, a single feature of the system under test to facilitate comparisons. In order to make sure that the comparisons are valid, benchmarks like those from the TPC require testing be done in a managed environment with a fixed configuration that can be completely described and replicated for future testing. Further, since the TPC benchmarks focus on transactional database systems they also require adherence to the ACID properties, notably (as we’ll see in a moment) the coherence property.

But, when you think about it, this benchmarking model isn’t really a good match for clouds, where the service model is designed to be dynamic, distributed and robust. One of the key selling points for cloud infrastructure is that it can grow and shrink with a particular user’s demand, and workload can be shifted to wherever it is most advantageously served. In this environment the hardware may change over time, as may parts of the system software stack. Furthermore creating a reliable distributed processing environment usually means replicating parts of the data, and making these data available in the presence of communications failures means relaxing some of the traditional guarantees on data consistency (Amazon’s cloud storage offering only guarantees eventual consistency, for example).

So, while traditional approaches to benchmarking, and traditional benchmarks for that matter, will provide some useful information about the performance of clouds, the traditional testing philosophy behind most benchmarks today doesn’t lend itself to creating a test of merit that enables comparison of two clouds with one another in a way that takes into account the very features that make them interesting technology solutions for certain classes of problems in the first place.

The general topic is dealt with ably in an interesting paper [PDF] from DBTest ’09. In that paper the authors outline what they’re looking for in a cloud benchmark: something that doesn’t require a static system configuration, reflects the ability of the cloud to adapt to changing load, assesses robustness to failures of various components, and includes the full cloud software stack rather than just one component.

That’s a pretty tall order, and amounts to something akin to not just being able to demonstrate that your 1996 Porsche 993 isn’t just faster than a Corvette, but that it’s cooler. Speed you can measure; “cool” is sufficiently general that it’s pretty hard to quantify. Still, you have to have a goal, and working on the problem is certainly worthwhile (not least because then my friends Steve and John could avoid a lot of pointless bar fights). The authors do manage a pretty reasonable suggestion for a benchmark in the paper, which I commend to your summer beach reading lists.

There are some cloud benchmarking efforts already well past the paper stage. Cloudstone, for example, is a benchmark out of UC Berkeley designed to measure the performance of clouds designed to run Web 2.0 applications. And there is also MalStone, a benchmark of more direct interest to the HPC crowd since it is designed specifically to allow the comparison of clouds designed for data intensive computing.

As described by Robert Grossman, the director of the National Center for Data Mining at the University of Illinois at Chicago and chair of the Open Cloud Consortium, MalStone is a “stylized analytic computation of a type that is common in data intensive computing.” The MalStone computation starts with a very large set of distributed files that document the date and time that users visited Web pages (including a user id), and also specify whether those users’ computers later become compromised by malware. The computation then goes through the files trying to identify Web pages that are possible sources of contamination by cross-referencing the browser history for each user id with records of whether the user’s machine is compromised. Web sites that figure prominently in the average browsing history of a cohort of machines that were subsequently compromised are suspect.

As Grossman points out, the task itself need not be a good way of finding Web sites hosting malware. It only needs to be a task sufficient to measure the performance of clouds for data intensive tasks:

We call MalStone stylized since we do not argue that this is a useful or effective algorithm for finding compromised sites. Rather, we point out that if the log data is so large that it requires large numbers of disks to manage it, then computing something as simple as this ratio can be computationally challenging. For example, if the data spans 100 disks, then the computation cannot be done easily with any of the databases that are common today. On the other hand, if the data fits into a database, then this statistic can be computed easily using a few lines of SQL.

There are two benchmarks, MalStone A and MalStone B. MalStone A computes a global figure for each Web site for all times included in the logs; MalStone B computes the figures by Web site by week. The datasets involved are quite large, with up to 100 TB of data.

MalStone A-10 uses 10 billion records so that in total there is 1 TB of data. Similarly, MalStone A-100 requires 100 billion records and MalStone A-1000 requires 1 trillion records. MalStone B-10, B-100 and B-1000 are defined in the same way.

You can read more about the benchmarks and get the actual source code for them at code.google.com/p/malgen/.

Earlier this summer Grossman and his team at the Open Cloud Consortium (OCC) announced results comparing Hadoop (the environment used by Facebook, Yahoo, and others) with the open-source cloud architecture Sector. Grossman describes Sector in a blog post as “an open source cloud written in C++ for storing, sharing and processing large data sets.” The OCC uses 10 GbE circuits on the National Lambda Rail (NLR) as the backbone for its testbed, and runs its tests over the NLR between San Diego, Los Angeles, Chicago and Washington, DC.

The preliminary results are interesting. They show significant differences between Hadoop and Sector, but also differences between Hadoop with Hadoop’s implementation of MapReduce, and Hadoop using Streams and coding MalStone in Python. The most significant differences are for MalStone B, where performance ranges from 841 minutes with Hadoop/MapReduce to 44 minutes with Sector. Even the Hadoop/Streams implementation, which is considerably faster than the MapReduce, comes in at nearly 143 minutes. The range there is 14 hours to 44 minutes, worst-case to best.

These results highlight the importance of making sure your cloud is designed to solve the problem at hand. And as MalStone and other cloud benchmarking efforts continue to evolve users will have even more robust tools to make informed decisions.

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!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues 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 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…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion 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…

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