The Morning Paper

JUNE 11, 2019

The goal here is to reduce the training times of DNNs by finding efficient parallel execution strategies, and even including its search time, FlexFlow is able to increase training throughput by up to 3.3x FlexFlow is evaluated over six real-world DNN benchmarks on two different GPU clusters. Expanding the search space.

Network 81

Network Strategy Hardware Benchmarking 81

Adrian Cockcroft

JANUARY 20, 2023

Here’s some predictions I’m making: Jack Dongarra’s efforts to highlight the low efficiency of the HPCG benchmark as an issue will influence the next generation of supercomputer architectures to optimize for sparse matrix computations. In early January a related paper was published by Satoshi Matsuoka et. petaflops, which is 0.8%

Architecture 52

Architecture Latency Benchmarking AWS 52

Tasktop

DECEMBER 12, 2022

The organization set an ambitious target of becoming three times more efficient at its delivery. . Flow Efficiency ® : The proportion of time flow items are actively worked on to the total time elapsed. To start, the leaders decided to use Flow Time to benchmark the team’s performance.

Metrics 52

Metrics Benchmarking Efficiency Speed 52

Tasktop

JULY 22, 2021

We must have a deep understanding of what changes will have the biggest impact on velocity, predictability, efficiency, throughput and so on.”. Are they meeting their benchmarks for adoption, are their outcomes improving, are the KPIs different? Flow : How efficient are you at delivering value to customers? “As

Metrics 98

Metrics DevOps Efficiency Benchmarking 98

The Morning Paper

JUNE 27, 2019

Systems researchers are doing an excellent job improving the performance of 5-year old benchmarks, but gradually making it harder to explore innovative machine learning research ideas. Convolutional Capsule primitives can be implemented reasonably efficiently on CPU but problems arise on accelerators (e.g. GPU and TPU).

Systems 87

Systems Tuning Programming Innovation 87

The Morning Paper

MAY 14, 2019

Last time around we looked at the DeathStarBench suite of microservices-based benchmark applications and learned that microservices systems can be especially latency sensitive, and that hotspots can propagate through a microservices architecture in interesting ways. ASPLOS’19. Seer in action.

Big Data 45

Big Data Cloud Performance Hardware 45

Alex Russell

APRIL 29, 2021

As an engineer on a browser team, I'm privy to the blow-by-blow of various performance projects, benchmark fire drills, and the ways performance marketing (deeply) impacts engineering priorities. With each team, benchmarks lost are understood as bugs. All modern browsers are fast, Chromium and Safari/WebKit included. CSS Custom Paint.

Media 145

Media Games Education Engineering 145

Percona

JUNE 22, 2023

” Here are additional metrics used to determine the reliability of a database, make adjustments that minimize downtime, and set benchmarks for meeting business continuity requirements. This can mean that instead of failing completely, a system continues to operate, but possibly at a reduced level of efficiency.

Availability 104

Availability Database Open Source Hardware 104

Alex Russell

JANUARY 30, 2024

This low-end cohort will filter through the device population for years to come, pulling our performance budgets down, even as renewed process improvement is unlocking improved power efficiency and performance at the high end of the first-sale market. Those folks may not find the next couple of years to their liking.

Performance 90

Performance Network Mobile Speed 90

High Scalability

SEPTEMBER 7, 2018

Pflop/s : fully synchronous tensorflow data-parallel training; 3.3 Once inside the function, there's nothing wrong with multi-threading to do the work as efficiently as possible. Matthew Dillon : This is *very* impressive efficiency. This is *very* impressive efficiency. They'll love you even more.

Internet 137

Internet Internet Scalability Lambda 137

Highly Scalable

MARCH 10, 2015

can be estimated by means of classification and regression models trained on historical data for customers who have received incentives in the past and those who did not. Propensity models are regression and classification models trained on customer data. The mathematical expectations of the net revenue in the equations (1.2)

Retail 152

Retail C++ Analytics Metrics 152

Smashing Magazine

AUGUST 24, 2021

TAPP is a transit application (for buses and trains). The header of the table announces TAPP’s values as a company, being efficient, trustworthy, and accessible. When you create a linter, you are setting a benchmark of quality for your documentation. Large preview ). The same is true with documentation.

Development 119

Development Benchmarking Code Engineering 119

High Scalability

NOVEMBER 25, 2019

Version7 (Future): Move all compute to the public cloud, carve out more services for impact isolation, dynamic resource pooling to manage pets and cattle efficiently. Once it's resolved our pipeline takes over and the ticket catches the next release train. This is the current architecture. How do you handle security?

Architecture 52

Architecture Cache Azure Storage 52