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

The Morning Paper

Compress objects, not cache lines: an object-based compressed memory hierarchy Tsai & Sanchez, ASPLOS’19. Existing cache and main memory compression techniques compress data in small fixed-size blocks, typically cache lines. Hotpads is a hardware-managed hierarchy of scratchpad-like memories called pads.

Cache 61
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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. This avoids thrashing caches too much for B and evens out the pressure on the L3 caches of the machine.

Cache 251
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Seeing through hardware counters: a journey to threefold performance increase

The Netflix TechBlog

We also see much higher L1 cache activity combined with 4x higher count of MACHINE_CLEARS. a usage pattern occurring when 2 cores reading from / writing to unrelated variables that happen to share the same L1 cache line. Cache line is a concept similar to memory page?—? Thread 0’s cache in this example.

Hardware 363
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PostgreSQL Performance Tuning: Optimizing Database Parameters for Maximum Efficiency

Percona

Key areas include: Configuration parameter tuning : This tuning involves altering variables such as memory allocation, disk I/O settings, and concurrent connections based on specific hardware and requirements. This not only results in cost savings by minimizing hardware requirements but also has the potential to decrease cloud expenses.

Tuning 52
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How to Assess MySQL Performance

HammerDB

This all sounded very similar to HammerDB TPROC-C workload (we will look at HammerDB TPROC-H (OLAP) another time), so it is easy for any reader to think both workloads are about the same. By default, HammerDB is designed to take advantage of database system caching mechanisms such as buffer caches, query caches, or statement caches.

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A thorough introduction to bpftrace

Brendan Gregg

C @ns: [256, 512) 10900 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ | [512, 1k) 18291 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@| [1k, 2k) 4998 |@@@@@@@@@@@@@@ | [2k, 4k) 57 | | [4k, 8k) 117 | | [8k, 16k) 48 | | [16k, 32k) 109 | | [32k, 64k) 3 | |. hardware Hardware counter-based instrumentation. Hit Ctrl-C to end. ^C

Latency 68
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File systems unfit as distributed storage backends: lessons from ten years of Ceph evolution

The Morning Paper

Breaking that assumption allowed Ceph to introduce a new storage backend called BlueStore with much better performance and predictability, and the ability to support the changing storage hardware landscape. But let’s take a quick look at the changing hardware landscape before we go on… The changing hardware landscape.

Storage 64