Daniel Lemire

FEBRUARY 8, 2019

I believe that all optimizing C/C++ compilers know how to pull this trick and it is generally beneficial irrespective of the processor’s architecture. I make my benchmarking code available. The idea is not novel and goes back to at least 1973 (Jacobsohn). What if d is a constant, but not known to the compiler? Can we do better?

C++ 279

C++ Benchmarking Hardware Testing 279

John McCalpin

FEBRUARY 17, 2025

The example below is for a 2005-era processor with 60 ns memory latency and 6.4 cache lines per core Available L1 cache miss concurrency: 10 cache lines per core 2023 processor: Xeon Max 9480, 56-core, “Sapphire Rapids” 307.2 cache lines -> 5.6 GB/s * 107 ns = 32870 Bytes -> 513 cache lines -> 9.2

Latency 71

Latency Hardware Cache Systems 71

Brendan Gregg

NOVEMBER 29, 2017

At Netflix, we've been using these technologies as they've been made available for instance types in the AWS EC2 cloud. It's amazing to recall that it was even possible to virtualize x86 before processors had hardware-assisted virtualization (Intel VT-x and AMD-V), which were added in 2005 and 2006. Nitro's performance is near-metal.

Virtualization 110

Virtualization AWS Hardware Storage 110

KeyCDN

MARCH 28, 2019

There are a myriad of options available when choosing which backend framework you want to work with. Laravel follows the MVC architectural pattern and was built to facilitate extensive backend development. Furthermore, it’s easy to build a robust API with the help of various HTTP utility methods and middleware available.

Programming 77

Programming Website Scalability Design 77

John McCalpin

APRIL 2, 2020

The presentation discusses a family of simple performance models that I developed over the last 20 years — originally in support of processor and system design at SGI (1996-1999), IBM (1999-2005), and AMD (2006-2008), but more recently in support of system procurements at The Texas Advanced Computing Center (TACC) (2009-present).

Benchmarking 59

Benchmarking Performance Latency Architecture 59

ScaleOut Software

JUNE 22, 2020

It can also take advantage of the elastic computing resources available in cloud infrastructures to quickly and cost-effectively scale throughput to meet changes in demand. They transparently distribute stored objects across the cluster’s servers and ensure that data is not lost if a server or network component fails.

Ecommerce 52

Ecommerce Cache Scalability IoT 52

ScaleOut Software

JUNE 22, 2020

It can also take advantage of the elastic computing resources available in cloud infrastructures to quickly and cost-effectively scale throughput to meet changes in demand. They transparently distribute stored objects across the cluster’s servers and ensure that data is not lost if a server or network component fails.

Ecommerce 52

Ecommerce Cache Scalability IoT 52