Benchmarking the AWS Graviton2 with KeyDB

DZone

database big data performance benchmarking performance analysis redis alternative ec2 image ec2stack hardware news keydbWe've always been excited about Arm so when Amazon offered us early access to their new Arm-based instances we jumped at the chance to see what they could do.

An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems

The Morning Paper

An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems Gan et al., A typical architecture diagram for one of these services looks like this: Suitably armed with a set of benchmark microservices applications, the investigation can begin! Hardware implications. ASPLOS’19.

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Scaling Benchmarks With More Robust UseNUMA Flag in OpenJDK

DZone

What happens when you run a Java application without checking your hardware configuration? java jdk openjdk benchmark nodes performanace jdk11 flag numa lagObviously, your application lags in terms of performance. For small applications, you need not worry, but for applications that require larger memory (in GB's), you need to take care of the configurations; otherwise, your application can suffer a lot. What Is NUMA?

The top 5 reasons to run your own database benchmarks

HammerDB

Some opinions claim that “Benchmarks are meaningless”, “benchmarks are irrelevant” or “benchmarks are nothing like your real applications” However for others “Benchmarks matter,” as they “account for the processing architecture and speed, memory, storage subsystems and the database engine.” That is why we run a workload designed exactly for this purpose as it gives us a “benchmark” 2.

How to maximize CPU performance for PostgreSQL 12.0 benchmarks on Linux

HammerDB

HammerDB doesn’t publish competitive database benchmarks, instead we always encourage people to be better informed by running their own. So over at Phoronix some database benchmarks were published showing PostgreSQL 12 Performance With AMD EPYC 7742 vs. Intel Xeon Platinum 8280 Benchmarks . ” Usually when benchmark results are surprising it is a major hint that something could be misconfigured and that certainly seems the case here, so what could it be?

SKP's Java/Java EE Gotchas: Clash of the Titans, C++ vs. Java!

DZone

One, by researching on the Internet; Two, by developing small programs and benchmarking. According to other comparisons [Google for 'Performance of Programming Languages'] spread over the net, they clearly outshine others in all speed benchmarks. performance c++ programming languages core java computing computer hardware programming & design complexity metrics optimization and algorithmic aspects platform independence

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From Heavy Metal to Irrational Exuberance

ACM Sigarch

I suggest it’s long past time to move beyond C and SPEC benchmarks and our exclusive focus on “metal” languages. There’s some work on hardware proposals for these systems, like Zhu et al., ACM SIGARCH Accelerators Architecture Benchmarks Programmability

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Compress objects, not cache lines: an object-based compressed memory hierarchy

The Morning Paper

Looking across a set of eight Java benchmarks, we find that only two of them are array dominated, the rest having between 40% to 75% of the heap footprint allocated to objects, the vast majority of which are small. Consider a B-Tree node from the B-tree Java benchmark: Uncompressed, it’s memory layout looks like (a) below. … to realize these insights, hardware needs to access data at object granularity and must have control over pointers between objects.

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PostgreSQL vs. Oracle: Difference in Costs, Ease of Use & Functionality

Scalegrid

Oracle support for hardware and software packages is typically available at 22% of their licensing fees.

A peculiar throughput limitation on Intel’s Xeon Phi x200 (Knights Landing)

John McCalpin

Hardware performance counter results for a simple benchmark code calling Intel’s optimized DGEMM implementation for this processor (from the Intel MKL library) show that about 20% of the dynamic instruction count consists of instructions that are not packed SIMD operations (i.e., Published DGEMM benchmark results for the Xeon Phi 7250 processor ( [link] ) show maximum values of about 2100 GFLOPS when using all 68 cores (a very approximate estimate from a bar chart).

Faster remainders when the divisor is a constant: beating compilers and libdivide

Daniel Lemire

The division by a power of two ( / (2 N )) can be implemented as a right shift if we are working with unsigned integers, which compiles to single instruction: that is possible because the underlying hardware uses a base 2. We also published our benchmarks for research purposes. I make my benchmarking code available. Not all instructions on modern processors cost the same. Additions and subtractions are cheaper than multiplications which are themselves cheaper than divisions.

The Performance Inequality Gap, 2021

Alex Russell

Hardware Past As Performance Prologue. Using a global ASP as a benchmark can further mislead thanks to the distorting effect of ultra-high-end prices rising while shipment volumes stagnate. But the hardware future is not evenly distributed, and web workloads aren't heavily parallel.

Spying on the floating point behavior of existing, unmodified scientific applications

The Morning Paper

Furthermore, as hardware and compiler optimisations rapidly evolve, it is challenging even for a knowledgeable developer to keep up. PARSEC is a set of benchmarks for multi-threaded programs. is a set of benchmarks for parallel computing developed by NASA.

What programming languages does HammerDB use and why does it matter?

HammerDB

HammerDB is a load testing and benchmarking application for relational databases. However, it is crucial that the benchmarking application does not have inherent bottlenecks that artificially limits the scalability of the database. Basic Benchmarking Concepts.

An analysis of performance evolution of Linux’s core operations

The Morning Paper

For the rest of us, if you really need that extra performance (maybe what you get out-of-the-box or with minimal tuning is good enough for your use case) then you can upgrade hardware and/or pay for a commercial license of a tuned distributed (RHEL). A micro-benchmark suite, LEBench was then built around tee system calls responsible for most of the time spent in the kernel. On the exact same hardware, the benchmark suite is then used to test 36 Linux release versions from 3.0

Amazon Redshift and the art of performance optimization in the cloud

All Things Distributed

Verifying benchmark claims. I picked these examples because they aren't operations that show up in standard data warehousing benchmarks, yet are meaningful parts of customer workloads. Verifying benchmark claims. That said, it is important to monitor benchmarks that help customers compare one cloud data warehousing vendor to another. I've noticed a troubling trend in vendor benchmarking claims over the past year.

HammerDB v4.0 New Features Pt1: TPROC-C & TPROC-H

HammerDB

A full understanding of why this is important requires some knowledge of the evolution of database hardware and software. The HammerDB TPROC-C workload by design intended as CPU and memory intensive workload derived from TPC-C – so that we get to benchmark at maximum CPU performance at a much smaller database footprint. more transactions than system B in the fully audited benchmark then the HammerDB result was also 1.5X

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Azure Virtual Machines for SQL Server Usage

SQL Performance

This removes the burden of purchasing and maintaining your hardware, storage and networking infrastructure, while still giving you a very familiar experience with Windows and SQL Server itself.

Progress Delayed Is Progress Denied

Alex Russell

As an engineer on a browser team, I'm privy to the blow-by-blow of various performance projects, benchmark fire drills, and the ways performance marketing (deeply) impacts engineering priorities. With each team, benchmarks lost are understood as bugs. is access to hardware devices.

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CheriABI: enforcing valid pointer provenance and minimizing pointer privilege in the POSIX C run-time environment

The Morning Paper

Last week we saw the benefits of rethinking memory and pointer models at the hardware level when it came to object storage and compression ( Zippads ). The protections are hardware implemented and cannot be forged in software. CHERI adds a new hardware data type for strongly protected C-language pointers, the CHERI capability (the evaluation uses an FPGA-based implementation). At hardware reset the boot code is granted maximally permissive architectural capabilities.

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Why OpenStack is like a Crowdfunded Viking Movie

VoltDB

Hardware Optimizers” want to get the maximum utilization out of hardware. These systems were designed to have a lifetime of half a decade or more, and rapidly changing hardware meant that the initial deployment had to be sized for 5-7 years out. Private Clouds made of commodity hardware are perceived as the logical solution to this problem. While the ultimate goal is still to save money, it’s human FTE hours as opposed to hardware and software costs.

Is It a Read Intensive or a Write Intensive Workload?

Percona

Because recognizing if the workload is read intensive or write intensive will impact your hardware choices, database configuration as well as what techniques you can apply for performance optimization and scalability. Let’s examine the TPC-C Benchmark from this point of view, or more specifically its implementation in Sysbench. The illustrations below are taken from Percona Monitoring and Management (PMM) while running this benchmark.

Further improved handling and reliability of OneAgent deployments

Dynatrace

Dynatrace OneAgent deployment and life-cycle management are already widely considered to be industry benchmarks for reliability and efficiency. Dynatrace news.

Why OpenStack is like a Crowdfunded Viking Movie

VoltDB

Hardware Optimizers” want to get the maximum utilization out of hardware. These systems were designed to have a lifetime of half a decade or more, and rapidly changing hardware meant that the initial deployment had to be sized for 5-7 years out. Private Clouds made of commodity hardware are perceived as the logical solution to this problem. While the ultimate goal is still to save money, it’s human FTE hours as opposed to hardware and software costs.

AMD EPYC 7002 Series Processors and SQL Server

SQL Performance

It will also use less power than a two-socket Intel server, with a lower hardware cost, and potentially lower licensing costs (for things like VMware). The initial reviews and benchmarks for these processors have been very impressive: AMD EPYC 7002 Series Rome Delivers a Knockout. AMD Rome Second Generation EPYC Review: 2x 64-core Benchmarked. TPC-H Benchmark Results with SQL Server 2017. TPC-E Benchmark Results with SQL Server 2017.

SQL Server 2016 – It Just Runs Faster: Always On Availability Groups Turbocharged

SQL Server According to Bob

When we released Always On Availability Groups in SQL Server 2012 as a new and powerful way to achieve high availability, hardware environments included NUMA machines with low-end multi-core processors and SATA and SAN drives for storage (some SSDs). As we moved towards SQL Server 2014, the pace of hardware accelerated. Our design needed to scale and be adaptable to the modern hardware on the market. These results are possible for anyone given a scalable hardware solution.

Beyond data and model parallelism for deep neural networks

The Morning Paper

FlexFlow is also given a device topology graph describing all the available hardware devices and their interconnections. Hardware connections between devices are modelled as special communication devices which can execute communication tasks. FlexFlow is evaluated over six real-world DNN benchmarks on two different GPU clusters. Beyond data and model parallelism for deep neural networks Jia et al., SysML’2019.

Is Intel Doomed in the Server CPU Space?

SQL Performance

A close monitoring of the hardware enthusiast community, including many of the most respected hardware analysts and reviewers paints an even more dire picture about Intel in the server processor space. For many years, I explicitly advised people not to run their SQL Server workloads on AMD hardware because of the much lower single-threaded CPU performance and consequently higher SQL Server core license costs.

Looking Ahead Beyond CMOS

ACM Sigarch

Finally, the paper An Expanded Benchmarking of Beyond-CMOS Devices Based on Boolean and Neuromorphic Representative Circuits makes the case that some of these new technologies may potentially outperform CMOS on alternative computing paradigms such as non-boolean circuits based on cellular neural networks. She is currently a Principal Hardware Engineer at Microsoft in the Quantum Architecture Group.

Machine learning systems are stuck in a rut

The Morning Paper

Systems researchers are doing an excellent job improving the performance of 5-year old benchmarks, but gradually making it harder to explore innovative machine learning research ideas. The thrust of the argument is that there’s a chain of inter-linked assumptions / dependencies from the hardware all the way to the programming model, and any time you step outside of the mainstream it’s sufficiently hard to get acceptable performance that researchers are discouraged from doing so.

HOW IT WORKS: SQL Server Scheduler Affinity

SQL Server According to Bob

The decision is performance driven. ​​ A memory node represents the memory associated with a group of CPUs from the physical hardware. MANUAL affinity provides the best, top end performance​​ (for benchmarks by utilizing L1 caches)​​ but is susceptible to noisy, CPU neighbors. HOW IT WORKS: SQL Server Scheduler Affinity SQL Server uses 3 types of affinity to control where the SQL Server worker threads execute.

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Kubernetes for Big Data Workloads

Abhishek Tiwari

A recent performance benchmark completed by Intel and BlueData using the BigBench benchmarking kit has shown that the performance ratios for container-based Hadoop workloads on BlueData EPIC are equal to and in some cases, better than bare-metal Hadoop [7]. Using default scheduler's node affinity feature you can ensure that certain pods only schedule on nodes with specialized hardware like GPU, memory-optimised, I/O optimised etc.

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

The Morning Paper

Last time around we looked at the DeathStarBench suite of microservices-based benchmark applications and learned that microservices systems can be especially latency sensitive, and that hotspots can propagate through a microservices architecture in interesting ways. When a QoS violation is predicted to occur and a culprit microservice located, Seer uses a lower level tracing infrastructure with hardware monitoring primitives to identify the reason behind the QoS violation.

Can You Afford It?: Real-world Web Performance Budgets

Alex Russell

Budgets are scaled to a benchmark network & device. Deciding what benchmark to use for a performance budget is crucial. Simulated packet loss and variable latency, however, can make benchmarking extremely difficult and slow. This, in turn, drives the single most important trend in setting the global web performance budget hardware baseline: the next billion users will largely come online when they can afford to.

SQL Server I/O Basics Chapter #1

SQL Server According to Bob

Example 1: ​​ Hardware failure (CPU board) Battery backup on the caching controller maintained the data. Important Always consult with your hardware manufacturer for proper stable media strategies. If you have a hardware or power failure, Microsoft strongly recommends the execution of a full DBCC ​​ CHECKDB ​​ suite and acquisition of the necessary backups to ensure data integrity.

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Performance Testing - Tools, Steps, and Best Practices

KeyCDN

Before you begin tuning your website or application, you must first figure out which metrics matter most to your users and establish some achievable benchmarks. Performance Testing Step by Step Once you’ve settled on which tools to use, here is a general guide to follow as you test your website’s performance: Set goals: Decide which metrics matter most to your users and establish some ideal benchmarks.

HammerDB MySQL and MariaDB Best Practice for Performance and Scalability

HammerDB

As is also the case this limitation is at the database level (especially the storage engine) rather than the hardware level. For anyone benchmarking MySQL with HammerDB it is important to understand the differences from sysbench workloads as HammerDB is targeted at a testing a different usage model from sysbench. driver: intel_pstate CPUs which run at the same hardware frequency: 0 . hardware limits: 1000 MHz - 3.80 current CPU frequency: Unable to call hardware

Upcoming of the learned data structures

Abhishek Tiwari

More importantly, if this works out well, this could lead to a radical improvement in performance by leveraging hardware trends such as GPUs and TPUs. The benchmarking was performed using 3 real-world data sets (weblogs, maps, and web-documents), and 1 synthetic dataset (lognormal). These areas benefit not only from neural networks ability to handle high-dimensional relationships but also recent hardware trends.

SQL Server I/O Basics Chapter #2

SQL Server According to Bob

Front-End Performance Checklist 2021

Smashing Magazine

On the other hand, we have hardware constraints on memory and CPU due to JavaScript parsing and execution times (we’ll talk about them in detail later). Geekbench CPU performance benchmarks for the highest selling smartphones globally in 2019. Front-End Performance Checklist 2021.

Front-End Performance Checklist 2020 [PDF, Apple Pages, MS Word]

Smashing Magazine

On the other hand, we have hardware constraints on memory and CPU due to JavaScript parsing times (we’ll talk about them in detail later). Geekbench CPU performance benchmarks for the highest selling smartphones globally in 2019.

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Egnyte Architecture: Lessons learned in building and scaling a multi petabyte content platform

High Scalability

The dedicated Security team runs automated security benchmark tests before every release. How are software and hardware upgrades rolled out? This is a guest post by Kalpesh Patel , an Engineer, who for Egnyte from home.

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