新九点自上线来，陆续有许多小的改进（比如API），今天，九点将迎来较为集中的更新：

1 “我的订阅” 名正言顺

我们将原有的豆瓣猜你会喜欢的博客&文章，从“我的订阅”移除，现在这里会是你完全掌控的博客阅读器。

新九点·我的订阅页面

2 九点首页有了 “你会感兴趣的文章”

现在起浏览九点首页，能读到九点根据你的阅读口味，推荐给你的文章。（不会太多，一次6条）

新九点·你会感兴趣的文章

3 未登陆用户，也能用九点了

不需要注册或登录，也能用九点阅读器。点击“我的订阅”，自己添加几个博客地址，这就开始阅读之旅。

新九点·未登陆也能用

As I previously mentioned, on Friday I attended the Drizzle Developer Day at Sun in Santa Clara. While there I had the chance to speak to the group while everyone ate their salad, pizza, and cookies.

The talk was titles "What Craigslist wants and needs from Drizzle" and is available as a Google Docs presentation here. I've also embedded a version of the slides below.

I should note here, as I did at the talk, that this presentation is neither comprehensive or completely representative. That is to say that I'm sure there are things I've forgotten. Plus, the fact that I was working with MySQL in other high-volume web shops before coming to Craiglist means that there's definitely some personal bias and pet peeves addressed in there too.

Anyway, that's what I presented.

Thanks to the fine folks at Sun (soon to be Oracle) for hosting and organizing the day. And special thanks to the Drizzle developers for getting together and showing the rest of us how things work and taking time to talk about their plans.

(comments)

The 2009 Percona Performance Conference finished up last week, and was overall a resounding success. Thanks to all of the speakers, O’Reilly, and Sun/MySQL for help making it happen! Most slides have been uploaded; look for the stragglers over the next couple of days.

Entry posted by Ryan Lowe | No comment

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We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up.
In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to many different learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time.
Specifically, we show that algorithms that fit the Statistical Query model can be written in a certain “summation form,” which allows them to be easily parallelized on multicore computers. We adapt Google’s map-reduce paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regression (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). Our experimental results show basically linear speedup with an increasing number of processors.

Read more about this study here (PDF - you can download also)

Scale-up solutions in the form of large SMPs have represented the mainstream of commercial computing for the past several years. The major server vendors continue to provide increasingly larger and more powerful machines. More recently, scale-out solutions, in the form of clusters of smaller machines, have gained increased acceptance for commercial computing.
Scale-out solutions are particularly effective in high-throughput web-centric applications.
In this paper, we investigate the behavior of two competing approaches to parallelism, scale-up and scale-out, in an emerging search application. Our conclusions show that a scale-out strategy can be the key to good performance even on a scale-up machine.
Furthermore, scale-out solutions offer better price/performance, although at an increase in management complexity.

Read more about scaling out/up and about the conclusions here (PDF - you can also download it)