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The Cloud High Performance Computing Market Set for Rapid Growth as Demand Surges, as per Maximize Market Research.

(EMAILWIRE.COM, November 01, 2024 ) The global Cloud High Performance Computing (HPC) market is poised for significant expansion, driven by growing demands in industries such as healthcare, finance, and automotive. Cloud HPC enables businesses to perform complex computations and simulations faster...




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The Coral Reefer Band to perform in Atlantic City

Keep the Party Going – A Tribute to Jimmy Buffett featuring The Coral Reefer Band. Thrilled to be able to announce that the The Coral Reefer Band will be playing at the Hard Rock Hotel’s …

The post The Coral Reefer Band to perform in Atlantic City first appeared on BuffettNews.com.




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PassMark PerformanceTest 11.0.1024 (Trial)

PerformanceTest enables you to benchmark your computer and compare it to a variety of baseline systems that are included in the database. You can select one or more computer mod....




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Performance Counters in Delphi, sample project and article

Implementing performance monitors in your app is very helpful for profiling, debugging and general satisfaction of the more qualified endusers. It's also a non-trivial excercise, to put it mildly. Microsoft have done their part in making it obscure and hard to use, so naturally, we can't leave it alone, can we? This particular implementation only implements raw and delta counters, but that covers almost anything you'll ever need. The basis is there for other types of counters, though, such as instance based counters and high-precision. Both precompiled and full project source is available.




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LXer: AmpereOne CPPC CPUFreq Schedutil vs. Performance Governor Benchmarks

Published at LXer: Similar to the ACPI CPUFreq and AMD/Intel P-State CPU frequency scaling driver and scaling governor benchmarks and power efficiency comparisons I routinely do on Phoronix, when...



  • Syndicated Linux News

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LXer: NanoPi M6: High-Performance SBC with Gigabit Ethernet and M.2 SSD Expansion

Published at LXer: The NanoPi M6 is a compact and open-source single-board computer (SBC) designed for high-performance applications, powered by the RK3588S System-on-Chip. Key features include an...



  • Syndicated Linux News

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LXer: Amarok 3.1.1 Promises Smoother Performance and UI Fixes

Published at LXer: Amarok 3.1.1, a free and open-source music player, has been released with enhanced toolbars, track editor fixes, and more. Read More......



  • Syndicated Linux News

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LXer: CachyOS September Update: Performance Gains and New Features

Published at LXer: Arch-based CachyOS's Sept '24 update boosts performance with PGO optimizations, new zlib-ng, Wayland default for SDDM, and more. Read More......



  • Syndicated Linux News

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LXer: Intel Xe2 Lunar Lake Graphics Performance Disappoints On Linux

Published at LXer: While I have been very eager to test out the Core Ultra 200V Lunar Lake series on Linux in part due to the new Xe2 integrated graphics, after several days of pushing a new Lunar...



  • Syndicated Linux News

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LXer: Linux 6.13 To Bring Big/Super Pages For The Raspberry Pi Graphics Driver - Better Performance

Published at LXer: While the Linux 6.12 merge window only ended this weekend and won't be out until November, already code is beginning to accumulate for DRM-Next of graphics driver improvements...



  • Syndicated Linux News

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Littelfuse Unveils High-Performance Ultra Junction X4-Class 200V Power MOSFETs for Enhanced Efficiency and Reliability

PRZOOM - Newswire (press release) - Tue, 12 Nov 2024 00:00:00 -0500, Chicago IL United States - Delivers industry-leading low on-state resistance, enabling simplified design and enhanced performance in battery energy storage and power supply applications - Littelfuse.com



  • Electronics / Instrumentation / RFID

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Processeur Arrow Lake-S : Intel promet de meilleures performances pour le mois de décembre

Ce n'est un secret pour personne, les performances des processeurs Arrow Lake-S ont été décevantes. Dans nos tests des 245K et 285K et même 265K, les mesures ont révélé que les performances n'étaient pas à la hauteur de nos espérances et d'autant plus en Gaming, ou le 14900K était souvent devant, tout comme pas mal de puces AMD d'ailleurs. Mais il semblerait qu'un grand nombre de facteurs puissent expliquer ces performances décevantes. […]

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Bienvenue sur GeForce NOW Performance : Les membres Priority bénéficient d'une mise à niveau instantanée

Découvrez l'adhésion Priority améliorée et renommée, désormais appelée Performance ; de plus, les Day Pass sont en vente pour une durée limitée et sept jeux rejoignent le cloud cette semaine. Il est temps de briller L'abonnement Performance conserve tous les mêmes avantages en matière de jeux et offre désormais aux membres une expérience de streaming améliorée sans frais supplémentaires. Dites bonjour à l'abonnement Performance. Les membres Performance peuvent streamer jusqu'à 1440p - une augmentation par rapport à la résolution précédente de 1080p - et découvrir les jeux dans des résolutions immersives et ultra-larges. Ils peuvent également sauvegarder leurs paramètres graphiques dans le jeu au fil des sessions de streaming, y compris pour les fonctionnalités NVIDIA RTX dans les titres pris en charge. Tous les membres actuels de Priority sont automatiquement mis à niveau vers Performance et peuvent profiter dès aujourd'hui de l'expérience de streaming améliorée. Les membres Performance se connecteront à des serveurs équipés de GeForce RTX pour une résolution allant jusqu'à 1440p. Les membres Ultimate continuent de bénéficier de la meilleure expérience de streaming : ils se connectent à des serveurs équipés de GeForce RTX 4080 avec une résolution allant jusqu'à 4K et 120 images par seconde, ou 1080p et 240 images par seconde en mode Compétitif pour les jeux prenant en charge la technologie NVIDIA Reflex. Les joueurs jouant sur le niveau gratuit verront désormais qu'ils diffusent en streaming à partir de plates-formes de base, avec des spécifications variables qui offrent un jeu en nuage d'entrée de gamme et sont optimisées pour la capacité. Il est temps de jouer. Au début de l'année prochaine, GeForce NOW proposera un temps de jeu mensuel de 100 heures pour continuer à offrir une qualité et une vitesse exceptionnelles, ainsi que des temps d'attente plus courts, aux membres Performance et Ultimate. Cette limite généreuse convient à 94 % des membres, qui profitent généralement du service dans ce laps de temps. Les membres peuvent vérifier le temps qu'ils ont passé dans le nuage via le portail de leur compte (voir l'exemple de capture d'écran ci-dessus). Jusqu'à 15 heures de jeu non utilisées seront automatiquement reportées sur le mois suivant pour les membres, et des heures supplémentaires peuvent être achetées au prix de 2,99 $ pour 15 heures supplémentaires de performance, ou de 5,99 $ pour 15 heures supplémentaires d'Ultimate. […]

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From Valencia, Spain, Emilio Modern Gypsy Unveils "Reborn" Music Video and Announces Global Performance Availability

Modern Spanish guitarist Emilio Modern Gypsy debuts his new music video, "Reborn," filmed in Tarragona, Spain, capturing his signature blend of flamenco fusion, modern and global rhythms. Now based in Valencia, Emilio is available for performances worldwide, bringing a renewed passion to his modern Spanish guitar. [PR.com]




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Brother MFC-8480DN High-Performance Laser All-in-One with Networking and Duplex Printing



Brother MFC-8480DN High-Performance Laser All-in-One with Networking and Duplex Printing
The MFC-8480DN is a high-performance laser all-in-one with networking and duplex printing for your business or small workgroup




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Pre and Post Race Performance

What you drink and eat before, during and after a race or intensive exercise workout affects your performance.




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Beets to Boot Your Running Performance

How eating beets can boost your endurance and your performance while running marathons.




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Does Colour Affect Athletic Performance?

The effects of the colour of a uniform on the success of an athlete or a sports team in winning.




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Sleep Medication and Athletic Performance

Here is everything you need to know if you are an athlete looking for a good night's sleep.




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Ways Sleep Can Improve Athletic Performance

There are many variables that affect athletic performance and sleep is one of the most important.




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Why Mobility Training Is Crucial for Fitness Performance

Mobility training improves performance or functional mobility in sports, during exercise workouts and in performing daily tasks by improving joint health, maximizing movement efficiency, helping to avoid injuries, and accelerating healing.




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KSI performs with Craig David and Anne-Marie at Wembley show




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SHARP announces Exceptional Performance and Launches its Future Workplace A3 MFPs

Marking their 50th Anniversary in the printing industry and the corporation’s 110th Anniversary in business, SHARP South Africa celebrated their success and unprecedented business growth during a partner event recently held in Johannesburg, South Africa. This event was a follow up from the Sharp summit in 2020 where representatives from SHARP Middle East & Africa [...]

The post SHARP announces Exceptional Performance and Launches its Future Workplace A3 MFPs appeared first on Braves News World.





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Mercedes AMG S 63 E Performance review: Absolute Power

Hanging onto the steering wheel for dear life while mumbling expletives is not what you imagine when you think of driving an S-Class. However, when it wears a ‘63’ badge, things are slightly different. More so when that badge gets red highlights, because that means the S-Class in question isn’t the usual, sophisticated, good boy, but its evil cousin. The Mercedes-AMG S 63 E-Performance is an 802hp limousine that is ready to rip a hole through time, and as its name suggests, it’s all about the performance. For once, the back seat takes a back seat in an S-Class. 

Mercedes-AMG S 63 powertrain and performance

The S 63 is a plug-in hybrid gone rogue. It gets a 4.0-litre twin-turbo V8, putting out 612hp and 900Nm, but then a 13kWh battery and electric motor on the rear axle put out an additional 190hp and 320Nm, bringing the total output to a hysterical 802hp and 1,430Nm. Deploying 1,430Nm of torque to the tarmac is like putting Usain Bolt on your home treadmill. There is so much grip that it feels like the car is trying to stretch the road beneath it every time you put your foot down. As a result, despite its 2.5-tonne heft, it can go from 0 to 100kph in just 3.4 seconds. The top speed is limited to 250kph, but if you opt for the Driver’s Pack, you can remove the limiter and get it up to 290kph. Clearly, range is not the priority for this hybrid. 

Beneath the skin lies some groundbreaking F1-inspired tech.

The numbers on the S 63 are intimidating, to say the least, and the experience from behind the wheel is, too. The car picks up the pace with zero lag, and you need to keep an eye on the speed readout; otherwise, it’s just a blur as you accelerate. 

What helps bring this earth-shattering performance to the road are two transmissions. The engine uses a speedshift 9-speed MCT, and the motor gets its own 2-speed transmission, because it spins at a different RPM from the engine. First gear on the motor is good enough for up to 140kph and only after that does it switch to second for a smoother torque transition. 

It will make you grin each time you find an empty stretch of road.

What you’ll be using most is the 9-speed with its paddle shifters. Needless to say, it is extremely responsive, and in Sport and Sport+, it’s extremely aggressive as well. When you push the car hard, shifts are immediate, and for total control, you can switch to ‘M’ or manual mode, where it will not upshift unless you tug at the right paddle. However, for everyday driving in the city, it isn’t the smoothest. There are noticeable jerks at low speeds, and even in Comfort mode, you’re constantly made aware of the massive power waiting to be unleashed. Then there is the exhaust, which, sadly, isn’t all-natural. A lot of it is fed through the speakers, and for the most part, you’ll barely hear it outside. Rev it high enough, though, and the V8 roar is very much there, along with pops and bangs that add to the drama. 

We’ve seen this powertrain in the GT 63 S E-Performance, where it had even more power (834hp) but a smaller (6.7kWh) battery. The S 63’s larger 13kWh battery results in a higher claimed electric-only range of 33km.  

Mercedes-AMG S 63 hybrid technology

The 13kWh battery pack uses Formula 1-derived tech and contains 1,200 individual cells that have a dedicated coolant line for thermal management. Not only is that better for packaging, but each cell can also be cooled individually, so you can push the car and not worry about overheating the battery. There are four recuperation modes—varying the level of regen—and you can charge the battery using a Type-2 port and the onboard 3.7kW AC charger. 

3.7kW charger can top up the battery.

Exclusive to the S 63 is a ‘B’ or Battery Hold mode that helps preserve the SOC. This allows you to switch to the all-electric ‘EL’ mode in heavy traffic and save some precious high-octane fuel. 

Mercedes-AMG S 63 ride and handline

Because this is an AMG S-Class, there are two contrasting ideologies at work—sportscar handling and luxurious ride comfort. If you want S-Class levels of soft and supple, this won’t do the trick. The ride is inherently firm, and although it gets adaptive air suspension, you will feel jitters in the cabin even in Comfort mode. It also gets active engine mounts, which help keep things composed, and once you find a winding ribbon of tarmac with long swooping bends, the AMG side of the dynamic package is really felt. That said, on our bad roads, the one thing that you absolutely have to be aware of is the low ground clearance. Sure, the air suspension has a raise function that helps you gain some crucial millimetres, but you still have to crab crawl over big speed bumps to avoid grazing the underbody. 

Gets vehicle raise function, but speedbumps need to be tackled with care.

In terms of handling, you don’t really expect big things from an S-Class, but AMG’s pedigree clearly flows through this car’s veins. The 4Matic+ system, along with 3-degree rear-wheel steering, makes a huge difference in the bends as well as in U-turns and three-point turns. Despite having the same long 3,216mm wheelbase as the standard S-Class, in the corners, it is agile and sharp. 

However, in seriously tight turns, its 2.5-tonne weight is what pulls it down, and at such points, all that handling tech can only do so much. 

Mercedes-AMG S 63 design

In terms of design, there are many telltale signs of it being an AMG. Edition 1 gets the ‘Night Pack’ that replaces every inch of bling with blackened bits and the ‘Carbon Pack’ that adds racy carbon-fibre elements to the air dams, splitter, side sill, mirrors and diffuser. Apart from that, it gets sporty bumpers with huge air dams, the massive ‘Panamericana’ grille and an AMG logo in place of the Mercedes-Benz emblem. There are also 21-inch wheels wrapped in Michelin Pilot Sport 4 tyres and carbon ceramic brakes (standard on the Edition 1).  

Optional carbon-ceramic brakes are a must.

The rear, which many will find themselves looking at, features quad exhaust tips and red highlights for the badges, letting everyone know that this is an E-Performance and something that’s not to be trifled with. 

Mercedes-AMG S 63 interior and features

On the inside, the S 63 prides itself on its AMG roots with a healthy dose of carbon fibre. The dashboard gets a carbon-fibre slab with a red weave within. The upper half also gets red stitching along with Nappa leather seats, which, though sporty, are superbly comfy as well. The new bit is the performance steering wheel that gives it full-blown AMG cred. Wrapped in Alcantara and leather, its plethora of touch buttons might seem intimidating at first, but it doesn’t take long to get used to. The steering also features context-sensitive AMG knobs that feature shortcuts to adjust the suspension, gearbox, drive modes and even the exhaust. 

Interior feels like a high-end lounge that also doubles up as a gym. S63 is the only S-Class you’d want to pilot yourself.

Also new are racier themes for the instrument cluster and telemetry, such as engine temperature, motor and battery readouts, and performance timing. The massive touchscreen in the centre is the nerve centre of the whole car, controlling everything from the AC to the chassis, and it’s super responsive. But rest assured, the screen has plenty of displays and data to satisfy your inner nerd.  

The rear seat may not be the best seat, given this is an AMG, but it’s always going to be special in an S-Class. You have plenty of room to stretch out, and all the bells and whistles like seat massaging, heating and ventilation, along with the ability to move the passenger seat ahead and fully stretch out.

Despite being a full-bore AMG, its rear seat offers the complete S-Class experience.

There is a superb Burmester 4D sound system, panoramic sunroof, incredibly comfy seats, rear entertainment screens, and even noise-cancelling headphones if the V8 is not sonorous enough for you. That said, with all that carbon fibre surrounding you and the grin on your chauffeur’s face, it won’t be long before you want to jump back into the driver’s seat. Also, since the S 63’s hybrid tech sits over the rear axle, the boot space is down to 310 litres, and you don’t get a spare wheel either. 

Mercedes-AMG S 63 price

1,430Nm of torque means every flex on the accelerator feels like being in the first seat on a roller coaster.

The S 63 E-Performance is priced from Rs 3.3 crore onwards, and the ‘Edition 1’ seen here, of which only three have been imported to India (and sold out), is listed at Rs 3.8 crore. It has supercar levels of power and all the luxury elements, and it is loaded with tech. Yet you’d struggle to justify the purchase. If you want a fun-to-drive car with lots of power, there are better options, and if you want a luxury car, the S-Class is already mighty impressive. It is difficult to see the logic, but as soon as you drive it, there is something about it that invokes your inner child. It won’t win track events or get you any drag race trophies. You won’t have a plausible justification. If you are the sort who looks for plausibility, this is definitely not the car for you. It’s a laugh, and you buy it because you can. If they ask you why you bought it, it’ll only take a quick spin to get them an answer. 

Also see: 

Mercedes India confirms over 12 new cars, SUVs coming this year

Next-gen Mercedes MB.EA Large EV platform cancelled amidst slow sales





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Mercedes AMG C 63 S E Performance launched at Rs 1.95 crore

Mercedes-AMG has launched the new C 63 S E Performance in India at Rs 1.95 crore. The C 63 S E Performance marks AMG’s third new car launch this year and its third plug-in hybrid in India, following the S 63 E Performance and the GT 63 S E Performance. Bookings have opened today, and deliveries are expected from April 2025 onwards.

  1. C 63 S E Performance pairs 476hp 4-cyl engine with 204hp motor
  2. Gets sporty AMG-specific changes inside and outside
  3. Buyers will get a complementary Nurburgring experience

Mercedes AMG C 63 S E Performance powertrain

Gone is the V8 engine that used to power the previous C 63. It’s been replaced with a 2.0-litre, four-cylinder, turbo-petrol engine that makes 476hp and 545Nm and is paired to a 9-speed automatic gearbox. The engine is paired to a rear axle-mounted electric motor that can put out a peak of 204hp and 320Nm. Total maximum output stands at 680hp and a whopping 1,020Nm of torque. Merc uses F1-derived tech for the turbocharger, which incorporates an electric motor that runs off the 400V electrical system, and helps the turbine spool faster.

Power is sent to all four wheels through the 4Matic+ system, which comes with a drift mode. The claimed 0-100kph time for the C 63 S E Performance is 3.4 seconds. The AMG Driver’s Package that's usually available as an option, is standard on the C 63. So top speed is 280kph.

The plug-in hybrid performance sedan gets a 6.1kWh battery pack (weighing 89kg), giving the C 63 an electric-only range of up to 13km. Four-wheel steering is also standard, which allows the rear wheels to turn up to 2.5deg in the opposite direction (up to 100kph), and up to 0.7deg in the same direction at speeds higher than that. The new C 63 gets eight drive modes – Electric, Comfort, Battery Hold, Sport, Sport+, RACE, Slippery and Individual – along three levels for its adaptive damping system – Comfort, Sport, and Sport+.

Mercedes AMG C 63 S E Performance interior, exterior

Compared to the standard C-Class, the AMG-spec model gets a vertically slatted grille with active shutters that open or close depending on the engine’s cooling needs. The restyled, more aggressive front and rear bumper increase the car's length by 83mm, and the wider front wheel arches make the performance version 76mm wider than the standard sedan. Lightweight 20-inch alloy wheels are standard.

Mercedes says AMG exclusive paint options like the Matt Graphite Grey Magno are customisable, and optional ceramic high-performance composite brakes can be specced instead of the standard ventilated and perforated metal ones. Naturally, the company offers a great deal of personalisation options with the C 63 S E Performance.

On the inside, the AMG and the standard model share the same layout, but this gets an all-black theme and an AMG-specific steering wheel. The ventilated sports seats in nappa leather and carbon fibre interior trim, a 12.3-inch touchscreen infotainment and head-up display, and the 710W, 15-speaker Burmester sound system are standard. AMG Performance seats are an optional extra. Along with the usual list of ADAS features, 7 airbags and a 360-degree camera are standard fit.

Mercedes-AMG says that every buyer of the C 63 S E Performance will get a complementary opportunity to race at the Nürburgring.

 

Mercedes AMG C 63 S E Performance price, rivals

While the Rs 1.95 crore Mercedes-AMG C 63 S E Performance has no direct rivals in India, other models that vie in this rarefied performance-focused space include the recently launched 550hp BMW M4 CS (Rs 1.89 crore) and the larger 500hp Porsche Panamera GTS (Rs 2.34 crore).

All prices, ex-showroom, India

Also see:

2024 Mercedes-AMG G 63 video review

Mercedes-AMG confirms future electric super SUV

11th edition of Mercedes-Benz Classic Car Rally slated for November 24




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How high-performance buildings are the next step towards a sustainable future

As urbanisation accelerates, India risks surpassing global benchmarks for energy efficiency and carbon emissions in buildings. In such a scenario, HPBs offer resilience through adaptive, self-sufficient structures. They promote social well-being by nurturing healthier indoor environments, including air quality




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Snapdragon 8 Elite 2: Early leak hints at over 20% CPU performance upgrade for Galaxy S26-series bound chipset



  • HardForum Tech News

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Experience Formula 1 engineering in the new Mercedes-AMG C 63 S E Performance sedan

Mercedes-Benz India has introduced the Mercedes-AMG C 63 S E PERFORMANCE, a hybrid sports sedan that blends high-performance engineering with Formula 1™-inspired technology




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Exploring the Luckeep X2: A High-Performance Urban E-Bike for All Terrains

This Luckeep X2 Stormtrooper E-Bike is Awesome We’ve tested a lot of electric bikes. I like some for certain reasons, others for other reasons, and some I really don’t care for much at all. I have to say that one thing I’ve found over the years is that e-bikes seem ... [continued]

The post Exploring the Luckeep X2: A High-Performance Urban E-Bike for All Terrains appeared first on CleanTechnica.




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Performance and Medication

Fredrica discusses the ethical problems of enhancing performance with medication.




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Robbie Williams to perform at 'iconic' venue

Pop star Robbie Williams will perform at Bath's Royal Crescent in June next year.




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Singer 'couldn't leave the house to perform'

Singer Catherine Lawless features in a film about agoraphobia, showing at Headfest in Bedford.




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Understanding ESB Performance & Benchmarking

ESB performance is a hot (and disputed topic). In this post I don't want to talk about different vendors or different benchmarks. I'm simply trying to help people understand some of the general aspects of benchmarking ESBs and what to look out for in the results.

The general ESB model is that you have some service consumer, an ESB in the middle and a service provider (target service) that the ESB is calling. To benchmark this, you usually have a load driver client, an ESB, and a dummy service.

+-------------+      +---------+      +---------------+
| Load Driver |------|   ESB   |------| Dummy Service |
+-------------+      +---------+      +---------------+

Firstly, we want the Load Driver (LD), the ESB and the Dummy Service (DS) to be on different hardware. Why? Because we want to understand the ESB performance, not the performance of the DS or LD.

The second thing to be aware of is that the performance results are completely dependent on the hardware, memory, network, etc used. So never compare different results from different hardware.

Now there are three things we could look at:
A) Same LD, same DS, different vendors ESBs doing the same thing (e.g. content-based routing)
B) Same LD, same DS, different ESB configs for the same ESB, doing different things (e.g. static routing vs content-based routing)
C) Going via ESB compared to going Direct (e.g. LD--->DS without ESB)

Each of these provides useful data but each also needs to be understood.

Metrics
Before looking at the scenarios, lets look at how to measure the performance. The two metrics that are always a starting point in any benchmark of an ESB here are the throughput (requests/second) and the latency (how long each request takes). With latency we can consider overall latency - the time taken for a completed request observed at the LD, and the ESB latency, which is the time taken by the message in the ESB. The ESB latency can be hard to work out. A well designed ESB will already be sending bytes to the DS before its finished reading the bytes the LD has sent it. This is called pipelining. Some ESBs attempt to measure the ESB latency inside the ESB using clever calculations. Alternatively scenario C (comparing via ESB vs Direct) can give an idea of ESB Latency. 

But before we look at the metrics we need to understand the load driver.

There are two different models to doing Load Driving:
1) Do a realistic load test based on your requirements. For example if you know you want to support up to 50 concurrent clients each making a call every 5 seconds on average, you can simulate this.
2) Saturation! Have a large number of clients, each making a call as soon as the last one finishes.

The first one is aimed at testing what the ESB does before its fully CPU loaded. In other words, if you are looking to see the effect of adding an ESB, or the comparison of one ESB to another under realistic load, then #1 is the right approach. In this approach, looking at throughput may not be useful, because all the different approaches have similar results. If I'm only putting in 300 requests a sec on a modern system, I'm likely to see 300 request a sec. Nothing exciting. But the latency is revealing here. If one ESB responds in less time than another ESB thats a very good sign, because with the same DS the average time per request is very telling.

On the other hand the saturation test is where the throughput is interesting. Before you look at the throughput though, check three things:
1) Is the LD CPU running close to 100%?
2) Is the DS CPU running close to 100%?
3) Is the network bandwidth running close to 100%?

If any of these are true, you aren't doing a good test of the ESB throughput. Because if you are looking at throughput then you want the ESB to be the bottleneck. If something else is the bottleneck then the ESB is not providing its max throughput and you aren't giving it a fair chance. For this reason, most benchmarks use a very very lightweight LD or a clustered LD, and similarly use a DS that is superfast and not a realistic DS. Sometimes the DS is coded to do some real work or sleep the thread while its executing to provide a more realistic load test. In this case you probably want to look at latency more than throughput.

Finally you are looking to see a particular behaviour for throughput testing as you increase load.
Throughput vs Load
The shape of this graph shows an ideal scenario. As the LD puts more work through the ESB it responds linearly. At some point the CPU of the ESB hits maximum, and then the throughput stabilizes.  What we don't want to see is the line drooping at the far right. That would mean that the ESB is crumpling under the extra load, and its failing to manage the extra load effectively. This is like the office worker whose efficiency increases as you give them more work but eventually they start spending all their time re-organizing their todo lists and less work overall gets done.

Under the saturation test you really want to see the CPU of the ESB close to 100% utilised. Why? This is a sign that its doing as much as possible. Why would it not be 100%? Two reasons: I/O, multi-processing and thread locks: either the network card or disk or other I/O is holding it up, the code is not efficiently using the available cores, or there are thread contention issues.

Finally its worth noting that you expect the latency to increase a lot under the saturation test. A classic result is this: I do static routing for different size messages with 100 clients LD. For message sizes up to 100k maybe I see a constant 2ms overhead for using the ESB. Suddenly as the message size grows from 100k to 200k I see the overhead growing in proportion to the message size.


Is this such a bad thing? No, in fact this is what you would expect. Before 100K message size, the ESB is underloaded. The straight line up to this point is a great sign that the ESB is pipelining properly. Once the CPU becomes loaded, each request is taking longer because its being made to wait its turn at the ESB while the ESB deals with the increased load.

A big hint here: When you look at this graph, the most interesting latency numbers occur before the CPU is fully loaded. The latency after the CPU is fully loaded is not that interesting, because its simply a function of the number of queued requests.

Now we understand the metrics, lets look at the actual scenarios.

A. Different Vendors, Same Workload
For the first comparison (different vendors) the first thing to be careful of is that the scenario is implemented in the best way possible in each ESB. There are usually a number of ways of implementing the same scenario. For example the same ESB may offer two different HTTP transports (or more!). For example blocking vs non-blocking, servlet vs library, etc. There may be an optimum approach and its worth reading the docs and talking to the vendor to understand the performance tradeoffs of each approach.

Another thing to be careful of in this scenario is the tuning parameters. Each ESB has various tuning aspects that may affect the performance depending on the available hardware. For example, setting the number of threads and memory based on the number of cores and physical memory may make a big difference.

Once you have your results, assuming everything we've already looked at is tickety-boo, then both latency and throughput are interesting and valid comparisons here. 

B. Different Workloads, Same Vendor
What this is measuring is what it costs you to do different activities with the same ESB. For example, doing a static routing is likely to be faster than a content-based routing, which in turn is faster than a transformation. The data from this tells you the cost of doing different functions with the ESB. For example you might want to do a security authentication/authorization check. You should see a constant bump in latency for the security check, irrespective of message size. But if you were doing complex transformation, you would expect to see higher latency for larger messages, because they take more time to transform. 

C. Direct vs ESB
This is an interesting one. Usually this is done for a simple static routing/passthrough scenario. In other words, we are testing the ESB doing its minimum possible. Why bother? Well there are two different reasons. Firstly ESB vendors usually do this for their own benefit as a baseline test. In other words, once you understand the passthrough performance you can then see the cost of doing more work (e.g. logging a header, validating security, transforming the message). 

Remember the two testing methodologies (realistic load vs saturation)? You will see very very different results in each for this, and the data may seem surprising. For the realistic test, remember we want to look at latency. This is a good comparison for the ESB. How much extra time is spent going through the ESB per request under normal conditions. For example, if the average request to the backend takes 18ms and the average request via the ESB takes 19ms, we have an average ESB latency of 1ms. This is a good result - the client is not going to notice much difference - less than 5% extra. 

The saturation test here is a good test to compare different ESBs. For example, suppose I can get 5000 reqs/sec direct. Via ESB_A the number is 3000 reqs/sec and via ESB_B the number is 2000 reqs/sec, I can say that ESB_A is providing better throughput than ESB_B. 

What is not  a good metric here is comparing throughput in saturation mode for direct vs ESB. 


Why not? The reason here is a little complex to explain. Remember how we coded DS to be as fast as possible so as not to be a bottleneck? So what is DS doing? Its really just reading bytes and sending bytes as fast as it can. Assuming the DS code is written efficiently using something really fast (e.g. just a servlet), what this is testing is how fast the hardware (CPU plus Network Card) can read and write through user space in the operating system. On a modern server hardware box you might get a very high number of transactions/sec. Maybe 5000req/s with each message in and out being 1k in size.

So we have 1k in and 1k out = 2k IO.
2k IO x 5000 reqs/sec x 8bits gives us the total network bandwidth of 80Mbits/sec (excluding ethernet headers and overhead).

Now lets look at the ESB. Imagine it can handle 100% of the direct load. There is no slowdown in throughput for the ESB. For each request it has to read the message in from LD and send it out to DS. Even if its doing this in pipelining mode, there is still a CPU cost and an IO cost for this. So the ESB latency of the ESB maybe 1ms, but the CPU and IO cost is much higher. Now, for each response it also has to read it in from DS and write it out to LD. So if the DS is doing 80Mbits/second, the ESB must be doing 160Mbits/second. 

Here is a picture.

Now if the LD is good enough, it will have loaded the DS to the max. CPU or IO capacity or both will be maxed out. Suppose the ESB is running on the same hardware platform as the DS. If the DS machine can do 80Mbit/s flat out, there is no way that the same hardware running as an ESB can do 160Mbit/s! In fact, if the ESB and DS code are both as efficient as possible, then the throughput via ESB will always be 50% of the throughput direct to the DS. Now there is a possible way for the ESB to do better: it can be better coded than the DS. For example, if the ESB did transfers in kernel space instead of user space then it might make a difference. The real answer here is to look at the latency. What is the overhead of adding the ESB to each request. If the ESB latency is small, then we can solve this problem by clustering the ESB. In this case we would put two ESBs in and then get back to full throughput.

The real point of this discussion is that this is not a useful comparison. In reality backend target services are usually pretty slow. If the same dual core server is actually doing some real work - e.g. database lookups, calculations, business logic - then its much more likely to be doing 500 requests a second or even less. 

The following chart shows real data to demonstrate this. The X-Axis shows increasing complexity of work at the backend (DS). As the effort taken by the backend becomes more realistic, the loss in throughput of having an ESB in the way reduces. So with a blindingly fast backend, we see the ESB struggling to provide just 55% of the throughput of the direct case. But as the backend becomes more realistic, we see much better numbers. So at 2000 requests a second there is barely a difference (around 10% reduction in throughput). 


In real life, what we actually see is that often you have many fewer ESBs than backend servers. For example, if we took the scenario of a backend server that can handle 500 reqs/sec, then we might end up with a cluster of two ESBs handling a cluster of 8 backends. 

Conclusion
I hope this blog has given a good overview of ESB performance and benchmarking. In particular, when is a good idea to look at latency and when to use throughput. 






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Enhanced TCP BBR performance in wireless mesh networks (WMNs) and next-generation high-speed 5G networks

TCP BBR is one of the most powerful congestion control algorithms. In this article, we provide a comprehensive review of BBR analysis, expanding on existing knowledge across various fronts. Utilising ns3 simulations, we evaluate BBR's performance under diverse conditions, generating graphical representations. Our findings reveal flaws in the probe's RTT phase duration estimation and unequal bandwidth sharing between BBR and CUBIC protocols. Specifically, we demonstrated that the probe's RTT phase duration estimation algorithm is flawed and that BBR and CUBIC generally do not share bandwidth equally. Towards the end of the article, we propose a new improved version of TCP BBR which minimises these problems of inequity in bandwidth sharing and corrects the inaccuracies of the two key parameters RTprop and cwnd. Consequently, the BBR' protocol maintains very good fairness with the Cubic protocol, with an index that is almost equal to 0.98, and an equity index over 0.95.




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Applying a multiplex network perspective to understand performance in software development

A number of studies have applied social network analysis (SNA) to show that the patterns of social interaction between software developers explain important organisational outcomes. However, these insights are based on a single network relation (i.e., uniplex social ties) between software developers and do not consider the multiple network relations (i.e., multiplex social ties) that truly exist among project members. This study reassesses the understanding of software developer networks and what it means for performance in software development settings. A systematic review of SNA studies between 1990 and 2020 across six digital libraries within the IS and management science domain was conducted. The central contributions of this paper are an in-depth overview of SNA studies to date and the establishment of a research agenda to advance our knowledge of the concept of multiplexity on how a multiplex perspective can contribute to a software developer's coordination of tasks and performance advantages.




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Does smartphone usage affect academic performance during COVID outbreak?

Pandemic has compelled the entire world to change their way of life and work. To control the infection rate, academic institutes deliver education online similarly. At least one smartphone is available in every home, and students use their smartphones to attend class. The study investigates the link between smartphone usage (SU) and academic performance (AP) during the pandemic. 490 data were obtained from various institutions and undergraduate students using stratified random sampling. These components were identified using factor analysis and descriptive methods, while the relationship of SU and AP based on gender classification was tested using Smart-PLS-SEM. The findings show that SU has a substantial relationship with academic success, whether done in class or outside of it. Even yet, the study found that SU and AP significantly impact both male and female students. Furthermore, the research focuses on SU outside and within the classroom to improve students' AP.




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An Investigation of Student Expectation, Perceived Performance and Satisfaction of E-textbooks




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Girls, Boys, and Bots: Gender Differences in Young Children’s Performance on Robotics and Programming Tasks

Prior work demonstrates the importance of introducing young children to programming and engineering content before gender stereotypes are fully developed and ingrained in later years. However, very little research on gender and early childhood technology interventions exist. This pilot study looks at N=45 children in kindergarten through second grade who completed an eight-week robotics and programming curriculum using the KIWI robotics kit. KIWI is a developmentally appropriate robotics construction set specifically designed for use with children ages 4 to 7 years old. Qualitative pre-interviews were administered to determine whether participating children had any gender-biased attitudes toward robotics and other engineering tools prior to using KIWI in their classrooms. Post-tests were administered upon completion of the curriculum to determine if any gender differences in achievement were present. Results showed that young children were beginning to form opinions about which technologies and tools would be better suited for boys and girls. While there were no significant differences between boys and girls on the robotics and simple programming tasks, boys performed significantly better than girls on the advanced programming tasks such as, using repeat loops with sensor parameters. Implications for the design of new technological tools and curriculum that are appealing to boys and girls are discussed.




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A Comparison of Student Academic Performance with Traditional, Online, And Flipped Instructional Approaches in a C# Programming Course

Aim/Purpose: Compared student academic performance on specific course requirements in a C# programming course across three instructional approaches: traditional, online, and flipped. Background: Addressed the following research question: When compared to the online and traditional instructional approaches, does the flipped instructional approach have a greater impact on student academic performance with specific course requirements in a C# programming course? Methodology: Quantitative research design conducted over eight 16-week semesters among a total of 271 participants who were undergraduate students en-rolled in a C# programming course. Data collected were grades earned from specific course requirements and were analyzed with the nonparametric Kruskal Wallis H-Test using IBM SPSS Statistics, Version 23. Contribution: Provides empirical findings related to the impact that different instructional approaches have on student academic performance in a C# programming course. Also describes implications and recommendations for instructors of programming courses regarding instructional approaches that facilitate active learning, student engagement, and self-regulation. Findings: Resulted in four statistically significant findings, indicating that the online and flipped instructional approaches had a greater impact on student academic performance than the traditional approach. Recommendations for Practitioners: Implement instructional approaches such as online, flipped, or blended which foster active learning, student engagement, and self-regulation to increase student academic performance. Recommendation for Researchers: Build upon this study and others similar to it to include factors such as gender, age, ethnicity, and previous academic history. Impact on Society: Acknowledge the growing influence of technology on society as a whole. Higher education coursework and programs are evolving to encompass more digitally-based learning contexts, thus compelling faculty to utilize instructional approaches beyond the traditional, lecture-based approach. Future Research: Increase the number of participants in the flipped instructional approach to see if it has a greater impact on student academic performance. Include factors beyond student academic performance to include gender, age, ethnicity, and previous academic history.




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The Impact of Teacher Gender on Girls’ Performance on Programming Tasks in Early Elementary School

Aim/Purpose: The goal of this paper is to examine whether having female robotics teachers positively impacts girls’ performance on programming and robotics tasks Background: Women continue to be underrepresented in the technical STEM fields such as engineering and computer science. New programs and initiatives are needed to engage girls in STEM beginning in early childhood. The goal of this work is to explore the impact of teacher gender on young children’s mastery of programming concepts after completing an introductory robotics program. Methodology: A sample of N=105 children from six classrooms (2 Kindergarten, 2 first grade, and 2 second grade classes) from a public school in Somerville, Massachusetts, participated in this research. Children were taught the same robotics curriculum by either an all-male or all-female teaching team. Upon completion of the curriculum, they completed programming knowledge assessments called Solve-Its. Comparisons between the performance of boys and girls in each of the teaching groups were made. Findings: This paper provides preliminary evidence that having a female instructor may positively impact girls’ performance on certain programming tasks and reduce the number of gender differences between boys and girls in their mastery of programming concepts. Recommendations for Practitioners: Practitioners should expose children to STEM role-models from a variety of backgrounds, genders, ethnicities, and experiences. Future Research: Researchers should conduct future studies with larger samples of teachers in order to replicate the findings here. Additionally, future research should focus on collecting data from teachers in the form of interviews and surveys in order to find out more about gender-based differences in teaching style and mentorship and the impact of this on girls' interest and performance in STEM.




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Using Educational Data Mining to Predict Students’ Academic Performance for Applying Early Interventions

Aim/Purpose: One of the main objectives of higher education institutions is to provide a high-quality education to their students and reduce dropout rates. This can be achieved by predicting students’ academic achievement early using Educational Data Mining (EDM). This study aims to predict students’ final grades and identify honorary students at an early stage. Background: EDM research has emerged as an exciting research area, which can unfold valuable knowledge from educational databases for many purposes, such as identifying the dropouts and students who need special attention and discovering honorary students for allocating scholarships. Methodology: In this work, we have collected 300 undergraduate students’ records from three departments of a Computer and Information Science College at a university located in Saudi Arabia. We compared the performance of six data mining methods in predicting academic achievement. Those methods are C4.5, Simple CART, LADTree, Naïve Bayes, Bayes Net with ADTree, and Random Forest. Contribution: We tested the significance of correlation attribute predictors using four different methods. We found 9 out of 18 proposed features with a significant correlation for predicting students’ academic achievement after their 4th semester. Those features are student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses, i.e., database fundamentals, programming language (1), and computer network fundamentals. Findings: The empirical results show the following: (i) the main features that can predict students’ academic achievement are the student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses; (ii) Naïve Bayes classifier performed better than Tree-based Models in predicting students’ academic achievement in general, however, Random Forest outperformed Naïve Bayes in predicting honorary students; (iii) English language skills do not play an essential role in students’ success at the college of Computer and Information Sciences; and (iv) studying an orientation year does not contribute to students’ success. Recommendations for Practitioners: We would recommend instructors to consider using EDM in predicting students’ academic achievement and benefit from that in customizing students’ learning experience based on their different needs. Recommendation for Researchers: We would highly endorse that researchers apply more EDM studies across various universities and compare between them. For example, future research could investigate the effects of offering tutoring sessions for students who fail core courses in their first semesters, examine the role of language skills in social science programs, and examine the role of the orientation year in other programs. Impact on Society: The prediction of academic performance can help both teachers and students in many ways. It also enables the early discovery of honorary students. Thus, well-deserved opportunities can be offered; for example, scholarships, internships, and workshops. It can also help identify students who require special attention to take an appropriate intervention at the earliest stage possible. Moreover, instructors can be aware of each student’s capability and customize the teaching tasks based on students’ needs. Future Research: For future work, the experiment can be repeated with a larger dataset. It could also be extended with more distinctive attributes to reach more accurate results that are useful for improving the students’ learning outcomes. Moreover, experiments could be done using other data mining algorithms to get a broader approach and more valuable and accurate outputs.




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Intellectual capital and its effect on the financial performance of Ethiopian private commercial banks

This study aims to examine the intellectual capital and its effect on the financial performance of Ethiopian private commercial banks using the pulic model. Quantitative panel data from audited annual reports of Ethiopian private commercial banks from 2011 to 2019 are collected. The robust fixed effect regression model has been adopted to investigate the effect of IC and the financial performance measures of the banks. The study results show a positive relationship between the value added intellectual coefficient (VAIC) and the financial performance of private commercial banks in Ethiopia. The study also revealed that the components of VAIC (i.e., human capital efficiency, capital employed efficiency, and structural capital efficiency) have a positive and significant effect on the financial performance of banks measured by return on asset and return on equity over the study periods. Practically, the results of the study could be useful for shareholders to consider IC as a strategic resource and hence emphasise these intangibles, and to the bank managers to benchmark themselves against the best competitors based on the level of efficiency rankings.




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Nexus between women directors and firm performance: a study on BSE 200 companies

The present study is a modest attempt to investigate the impact of gender diversity on firm performance of BSE 200 listed companies. The study is based on the secondary data collected from the EMIS database and the corporate governance reports for a period of eight years, i.e., from 2012 to 2019. Sample size of the present study is 174 Indian companies listed in the Bombay Stock Exchange. The study has employed multiple regression models by considering the endogeneity issue to empirically test the impact of gender diversity on firm performance in Indian context. Based on the multiple regression models, we find that the impact of gender diversity is positive and significant on the market-based measure of firm performance. However, the impact becomes negative significant when firm performance was measured by accounting based measure of firm performance.




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'CSR, sustainability and firm performance linkage' current status and future dimensions - a bibliometric review analysis

Corporate social responsibility (CSR) and sustainability are gaining worldwide recognition. The question of whether CSR and sustainability programs benefit an organisation's financial success is still being debated. This study aims to verify this phenomenon by examining the current literature pattern on this relationship using bibliometric and systematic review analysis. It further provides a taxonomy for understanding this association. VOSviewer is used to obtain comprehensive dataset mapping and clustering in the field. The manuscript offers promising insights regarding academia by assessing the pattern of publication trends, the most influential author in the area, and analysing the methodological and theoretical underpinnings of CSR, sustainability and firm performance linkage. The outcome of this study provides exploratory insights into research gaps and avenues for future research.




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Effective inventory management among Malaysian SMEs in the manufacturing sector towards organisational performance

In several manufacturing firms, inventory constitutes most of the current assets, and this underscores the importance of inventory management as a fundamental issue for the majority of the firms irrespective of their sizes. Therefore, the purpose of this research is to assess the factors that influence the effectiveness of inventory management of Malaysian SMEs in the manufacturing sector. The study employs PLS-SEM technique to test the hypotheses. The main findings show that documentation and records, inventory control system and qualified personnel have positive effects on effective inventory management of Malaysian SMEs in the manufacturing sector. The study also reveals that effective inventory management has a mediating effect on the relationship between documentation and records, inventory control system, qualified personnel and organisational performance. Therefore, the study recommends that Malaysian SMEs in the manufacturing sector should improve their approaches to embracing effective inventory management practices in order to enhance organisational performance.




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Assessing supply chain risk management capabilities and its impact on supply chain performance: moderation of AI-embedded technologies

This research investigates the correlation between risk management and supply chain performance (SCP) along with moderation of AI-embedded technologies such as big data analytics, Internet of Things (IoT), virtual reality, and blockchain technologies. To calculate the results, this study utilised 644 questionnaires through the structural equation modelling (SEM) method. It is revealed using SmartPls that financial risk management (FRM) is positively linked with SCP. Second, it was observed that AI significantly moderates the connection between FRM and SCP. In addition, the study presents certain insights into supply chain and AI-enabled technologies and how these capabilities can beneficially advance SCP. Besides, certain implications, both managerial and theoretical are described for the supply chain managers along with limitations for future scholars of the world.




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Application of artificial intelligence in enterprise human resource management and employee performance evaluation

With the rapid development of Artificial Intelligence (AI) technology, significant breakthroughs have been made in its application in many fields. Especially, in the field of enterprise human resource management and employee performance evaluation, AI has demonstrated its powerful ability to optimise and improve performance. This study explores the application of AI in enterprise human resource management and how to use AI to evaluate employee performance. The research includes analysing and comparing existing AI-driven human resource management models, evaluating how AI can help improve employee performance and leadership styles, and designing and developing human resource management computer systems for enterprise employees. Through empirical research and case analysis, this study proposes a new AI-optimised employee performance evaluation model and explores its application and effect in practice. In general, the application of AI can improve the efficiency and accuracy of enterprise human resource management, and provide new possibilities for employee performance evaluation. At present, artificial intelligence technology has been widely used in various fields of daily life, especially in corporate human resource management, providing better support for the development of enterprises.