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USENIX LISA2021 Computing Performance: On the Horizon

Brendan Gregg

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. Note that my predictions in this talk may be wrong, but they should be thought provoking.

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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

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 (..)

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USENIX LISA2021 Computing Performance: On the Horizon

Brendan Gregg

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. Note that my predictions in this talk may be wrong, but they should be thought-provoking.

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

Scalegrid

Recognized as the fastest growing database by popularity, PostgreSQL was named the DBMS of the year in both 2018 and 2017 by DB-Engines, and continues to grow in popularity in 2019. pg_repack – reorganizes tables online to reclaim storage. In fact, PostgreSQL is so popular, 11.5% So Which Is Best?

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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

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 (..)

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Kubernetes for Big Data Workloads

Abhishek Tiwari

In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. faster access to external storage and data locality (I/O, bandwidth). optimised container networking and security. Storage provisioning. But Kubernetes storage is evolving quite quickly.

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The Ethics of Web Performance

Tim Kadlec

Sites that use an excess of resources, whether on the network or on the device, don’t just cause slow experiences, but can leave entire groups of people out. I recently profiled a page on a Pixel 2 (released in 2017), and an Alcatel 1x (released in 2018). Hardware gets better, sure. On the Alcatel 1x, it took ~65 seconds.