Search Results for "high performance computing"

LoopVectorization.jl is a Julia package for accelerating numerical loops by automatically applying SIMD (Single Instruction, Multiple Data) vectorization and other low-level optimizations. It analyzes loops and generates highly efficient code that leverages CPU vector instructions, making it ideal for performance-critical computing in fields such as scientific computing, signal processing, and machine learning.

TimerOutputs.jl is a lightweight Julia package that provides a structured way to measure and report the execution time of different parts of code. It is particularly useful for performance profiling in scientific computing, allowing developers to annotate sections of code and generate readable timing summaries. TimerOutputs.jl supports nested timers and formatted output to both terminal and files, helping users easily identify bottlenecks in their programs.

...If you want battle-hardened Node.js applications that can be monitored and kept alive forever, PM2 is the way to go. PM2 lets you manage, maintain and increase Node.js performance. It is constantly assailed by over 1800 tests so you can deliver high quality applications, and deploy confidently and more often. PM2 is cross-platform and arguably the most widely-used process manager for Node.js with over 100 million downloads. Setting it up is easy, it just takes a couple of minutes and one simple command.

High-performance tabular data export tool. Support Xlsx/CSV as mixed input of tabular data. Support JSON/Golang/C#/Java/Lua/binary source, data, and type output. Automatic cell data format checking, accurate to cell errors. Support predefined enumeration can use Chinese enumeration type. Support table splitting, support multi-person collaboration.

CAT is a distributed application performance monitoring and alerting system that specializes in tracking request flows, exceptions, and metrics across microservice ecosystems. It offers real-time dashboards showing throughput, response times, error rates, and service dependency graphs to help operations and development collaborate on reliability issues. In addition to metrics, it enables tracing—propagating context across RPC boundaries so problems like latency spikes or failed calls can be...

We provide MATLAB and Verilog Models of GeAr, and previously proposed adders (ACA-I, ETAII, ACA-II and GDA) at http://sourceforge.net/projects/approxadderlib/ GeAr is a low latency Generic Accuracy Configurable Adder that provides a higher number of potential configurations compared to state-of-the-art approximate adders, thus enabling a high degree of flexibility and trade-off between performance and output quality. These MATALB and Verilog models can allow software programmer as well as hardware designers to evaluate their code and design. To the best of our knowledge, this is the first open-source library of approximate adders that facilitates reproducible comparisons and further research and development in this direction across various layers of design abstraction. ...

A collection of software benchmarks developed to compare the performance of Java with C on identical code. No language libraries were used to avoid implementation differences. Some of the benchmarks are also implemented in Python and Scala. There are benchmarks for bit twiddling, numerical computing, data structure manipulation, concurrent computing, callouts to native libraries, and, graphics processing units (GPU) utilization.

A very high speed memory manager (aka malloc, aka heap manager), substantially faster than other fast malloc implementations tested (nedmalloc, tcmalloc, TBBMalloc, FastMM4) on small allocations in multithreaded scenarios. And some benchmarks for measuring heap performance.