在代码大模型（Code LLMs）的预训练中，行业内长期存在一种惯性思维，即把所有编程语言的代码都视为同质化的文本数据，主要关注数据总量的堆叠。然而，现代软件开发本质上是多语言混合的，不同语言的语法特性、语料规模和应用场景差异巨大。如果忽略这些差异，笼统地应用通用的 Scaling Laws，往往会导致性能预测偏差和算力浪费。

在代码大模型（Code LLMs）的预训练中，行业内长期存在一种惯性思维，即把所有编程语言的代码都视为同质化的文本数据，主要关注数据总量的堆叠。然而，现代软件开发本质上是多语言混合的，不同语言的语法特性、语料规模和应用场景差异巨大。如果忽略这些差异，笼统地应用通用的 Scaling Laws，往往会导致性能预测偏差和算力浪费。

Distributed computing is the simultaneous use of more than one computer to solve a problem. It is often used for problems that are so big that no individual computer can handle them. This method of ...

AUSTIN, Texas, June 7, 2022 /PRNewswire/ -- Dask, an open-source Python-native parallel computing library, has announced its latest rebrand. Dask's rebranding includes a new visual style throughout ...

This sponsored post from Intel highlights how today’s enterprises can achieve high levels of parallelism in large scale Python applications using the Intel Distribution for Python with Numba. The ...

Nvidia has been more than a hardware company for a long time. As its GPUs are broadly used to run machine learning workloads, machine learning has become a key priority for Nvidia. In its GTC event ...