3 مستودعات
On-disk storage of data in compressed blocks to process datasets exceeding system memory.
Distinct from Data Compression: Distinct from general data compression: specifically for managing massive training datasets that cannot fit in RAM.
Explore 3 awesome GitHub repositories matching data & databases · External Memory Block Compression. Refine with filters or upvote what's useful.
XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m
Handles massive datasets by storing data in compressed on-disk blocks and loading them as needed.
CppGuide is a curated collection of educational resources and practical guides focused on C++ server development, Linux kernel internals, concurrent programming, network protocols, and security exploitation. It provides structured learning paths for backend developers, covering everything from interview preparation to building high-performance network servers and understanding operating system fundamentals. The guide distinguishes itself by offering in-depth, hands-on tutorials that walk through real-world implementations, including building a Redis-like server from scratch, designing custom
Compresses swapped-out pages in RAM before they reach disk to reduce memory pressure.
qsv is a high-performance command line toolkit for querying, transforming, and analyzing comma-separated value files. It functions as a data wrangling interface and a tabular data profiler, featuring a query engine capable of executing SQL statements and joins directly on flat files without requiring a database. The project is distinguished by its ability to process massive datasets that exceed available system memory. This is achieved through disk-based external memory processing, including multithreaded merge sorting, on-disk hash tables for deduplication, and lightweight file indexing for
Uses disk-based external memory to sort and deduplicate massive datasets that exceed available system RAM.