Search Results for "cpu scheduling"

TileLang is a Pythonic domain-specific language for writing high-performance GPU, CPU, and accelerator kernels without dropping fully into low-level vendor code. Its compiler infrastructure is built on TVM and targets workloads such as GEMM, dequantization, FlashAttention, and linear attention. Developers can work at different abstraction levels while still controlling memory layout, scheduling, pipelines, and hardware-specific behavior.

Bend is an interactive programming environment (REPL) built on top of the Kotlin language, designed to allow users to explore, experiment, and learn Kotlin in a live, feedback-driven manner. The tool lets you define variables, functions, or values at the prompt and iteratively refine them—immediately seeing output and types—while preserving state across commands. It emphasizes discoverability and experimentation: users can inspect functions, call them on sample inputs, and evolve logic...

...It’s designed around a low-overhead producer/consumer model: instrumented components (“producers”) write binary events into shared memory buffers and a collector (“service”) reliably streams them to storage. The data model spans kernel and userspace, so you can stitch together CPU scheduling, app lifecycles, binder/IPC hops, GPU work, power and thermal signals, file I/O, heap samples, and more into a single coherent timeline. Perfetto’s ecosystem includes a web-based UI that can load multi-GB traces directly in the browser and an offline “trace processor” that exposes the trace as a queryable SQL-like table schema for deep analysis and automation. ...

UIforETW is a Windows performance tracing companion that wraps the Event Tracing for Windows (ETW) toolchain in an approachable GUI. It standardizes trace collection profiles, launches WPR/xperf with the right providers, and organizes the resulting .etl files for repeatable investigations. The tool streamlines the entire loop—record, annotate, open in WPA/XperfView—so engineers can focus on finding scheduling stalls, I/O bottlenecks, GC pauses, or GPU hitches instead of memorizing...

...It contains a series of high-quality base components, such as a go-style coroutine, coroutine-based network programming framework, command line, and config file parser, high-performance log library, unit testing framework, JSON library, etc. co has implemented a go-style coroutine, which has the following features. Multi-thread scheduling, the default number of threads is the number of system CPU cores. Shared stack, coroutines in the same thread share several stacks (the default size is 1MB), and the memory usage is low. Simple test on Linux shows that 10 millions of coroutines only take 2.8G of memory (for reference only). There is a flat relationship between coroutines, and new coroutines can be created from anywhere (including in coroutines). ...

Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes...

...NeuraNEP handles microthread scheduling, synchronization, distribution and communication. This project is a fork of SpikeOS (sourceforge.net/projects/spikeos) and represents a major update to that code base, including a scripting interface and low-level rewrite of several components. SpikeOS was oriented towards computational modeling. NeuraNEP is oriented toward neural network research.

A parallel-programming framework for concurrently running large numbers of small autonomous jobs, or microthreads, across multiple cores in a CPU or CPUs in a cluster. Each microthread is conceptually similar to a task in Ada and it is much lighter weight than an operating system thread. SpikeOS was designed to handle millions of microthreads, for example in a neural network hosting millions of spiking model neurons. SpikeOS handles microthread scheduling, synchronization, distribution and communication. *** This project has been forked. ...

Tpl makes it easy to serialize your C data using just a handful of API functions. The data is stored in its native binary form for maximum efficiency. C, Perl and XML supported. Data is portable across CPU types and OS's from Unix to Mac to Windows.

This program allows one to virtually see how the different process scheduling algorithms work. The purpose of this program is to act as a recruiting tool for future computer engineers as well as a teaching tool for future Modern OS classes.