This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Hardware virtualization for cloud computing has come a long way, improving performance using technologies such as VT-x, SR-IOV, VT-d, NVMe, and APICv. The latest AWS hypervisor, Nitro, uses everything to provide a new hardware-assisted hypervisor that is easy to use and has near bare-metal performance. I'd expect between 0.1%
AWS Graviton2); for memory with the arrival of DDR5 and High Bandwidth Memory (HBM) on-processor; for storage including new uses for 3D Xpoint as a 3D NAND accelerator; for networking with the rise of QUIC and eXpress Data Path (XDP); and so on. I also wrote about these topics in detail for my recent [Systems Performance 2nd Edition] book.
Adoption is now really starting to explode in 2015 as more and more businesses understand the power analytics has to empower their organizations. In the past analytics within an organization was the pinnacle of old style IT: a centralized data warehouse running on specialized hardware. Cloud enables self-service analytics.
On multi-core machines – which is the majority of the hardware nowadays – and in the cloud, we have multiple cores available for use. now has a version which will support parallelism for SELECT queries (utilizing the read capacity of storage nodes underneath the Aurora cluster). With faster disks (i.e. AWS Aurora (based on MySQL 5.6)
Make sure your system can handle next-generation DRAM,” [link] Nov 2011 - [Hruska 12] Joel Hruska, “The future of CPU scaling: Exploring options on the cutting edge,” [link] Feb 2012 - [Gregg 13] Brendan Gregg, “Blazing Performance with Flame Graphs,” [link] 2013 - [Shimpi 13] Anand Lal Shimpi, “Seagate to Ship 5TB HDD in 2014 using Shingled Magnetic (..)
AWS Graviton2); for memory with the arrival of DDR5 and High Bandwidth Memory (HBM) on-processor; for storage including new uses for 3D Xpoint as a 3D NAND accelerator; for networking with the rise of QUIC and eXpress Data Path (XDP); and so on. I also wrote about these topics in detail for my recent [Systems Performance 2nd Edition] book.
Hardware virtualization for cloud computing has come a long way, improving performance using technologies such as VT-x, SR-IOV, VT-d, NVMe, and APICv. The latest AWS hypervisor, Nitro, uses everything to provide a new hardware-assisted hypervisor that is easy to use and has near bare-metal performance. I'd expect between 0.1%
Make sure your system can handle next-generation DRAM,” [link] , Nov 2011 [Hruska 12] Joel Hruska, “The future of CPU scaling: Exploring options on the cutting edge,” [link] , Feb 2012 [Gregg 13] Brendan Gregg, “Blazing Performance with Flame Graphs,” [link] , 2013 [Shimpi 13] Anand Lal Shimpi, “Seagate to Ship 5TB HDD in 2014 using Shingled Magnetic (..)
A then-representative $200USD device had 4-8 slow (in-order, low-cache) cores, ~2GiB of RAM, and relatively slow MLC NAND flash storage. Hardware Past As Performance Prologue. Regardless, the overall story for hardware progress remains grim, particularly when we recall how long device replacement cycles are: Tap for a larger version.
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). In real-life world, most products aren’t even close: an median bundle size today is around 417KB , which is up 42% compared to early 2015. Assets Optimizations.
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). In real-life world, most products aren’t even close: an average bundle size today is around 400KB , which is up 35% compared to late 2015. We could also go beyond the bundle size budget though.
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). compared to early 2015. In 2015, Google introduced Brotli , a new open-source lossless data format, which is now supported in all modern browsers. Assets Optimizations.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content