Benchmarking Cache Code Hardware 
Scalegrid
JANUARY 25, 2024
Key metrics like throughput, request latency, and memory utilization are essential for assessing Redis health, with tools like the MONITOR command and Redis-benchmark for latency and throughput analysis and MEMORY USAGE/STATS commands for evaluating memory. <code> 127.0.0.1:6379> <code> 127.0.0.1:6379>
HammerDB
APRIL 19, 2023
GHz 4th Generation Intel Xeon Scalable processors (code-named Sapphire Rapids) Up to 20% higher compute performance than z1d instances Up to 50 Gbps of networking speed Up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (EBS) We can also verify these capabilities by running some simple benchmarks on the different subsystems.
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Brendan Gregg
MARCH 16, 2024
Apart from library code, maybe your application doesn't have frame pointers either, in which case everything is broken. Only in extreme circumstances does the cost (in processor time and I-cache footprint) translate to a tangible benefit - circumstances which usually resort to hand-coded assembly anyway.
Abhishek Tiwari
DECEMBER 13, 2017
More importantly, if this works out well, this could lead to a radical improvement in performance by leveraging hardware trends such as GPUs and TPUs. They demonstrated that neural nets based learned index outperforms cache-optimized B-Tree index by up to 70% in speed while saving an order-of-magnitude in memory. Learned indexes.
HammerDB
JUNE 27, 2022
HammerDB is a software application for database benchmarking. It enables the user to measure database performance and make comparative judgements about database hardware and software. Databases are highly sophisticated software, and to design and run a fair benchmark workload is a complex undertaking. Derived Workloads.
The Morning Paper
MAY 12, 2019
An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems Gan et al., A typical architecture diagram for one of these services looks like this: Suitably armed with a set of benchmark microservices applications, the investigation can begin! Hardware implications.
ACM Sigarch
OCTOBER 12, 2020
First, its origin was in a monoculture (the browser) wher e there was no need for compatibility with legacy code. Unfortunately, languages like Python have proven resistant to efficient implementation, partly because of their design, and partly because of limitations imposed by the need to interop with C code. MICRO 15 , Gope et al.,
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