One way to detect probable false-positive bitfield errors in cross platform Gecko code is to check whether Memcheck reports errors at the same places. I created a suppressions file, which tells Dr Memory to hide errors I identified as clearly false. A second line of defense is to add extra initialisation code for bitfields purely in order to keep Dr Memory happy. Neither of these are really what one wants to do, though.
The effect is to create a lot more undefined value errors needing investigation. This increases usability at the expense of losing undefined value checking.
The use of Weblogs (blogs) by librarians and libraries to disseminate information
I did this some years back, in the approach to Firefox 4, but the situation has drifted since then. That is, free from invalid memory accesses and uses of undefined values. This involved fixing around 25 bugs, mostly to do with uninitialised values. They fall into three categories:.
The majority of the bugs are not to do with memory accesses in invalid places. I suspect most invalid accesses have already been picked up by our ASan runs. The severity of the bugs varies. Any bug picked up by Valgrind might have serious consequences, as a result of making the computation depend on unknown values, either undefined ones, or ones taken from unknown places in memory.
Some of the bugs seem fairly harmless. So, is it truly clean? Well, not entirely, for various reasons.
weblogs: a history and perspective
For one thing this is dynamic testing, of course, so it only covers scenarios that the test set exercises. Thirdly, this tests core Gecko code and the Linux specifics. One noticeable thing, if you play the run-Valgrind-and-fix-what-you-see game long enough, is that we can drive the observable memory errors in our code down to zero. That is, in anything that we build from source. Eventually we arrive at a situation where — at least so it seems — our code is cleaner than some of the other libraries it relies on.
Valgrinding Mochitests is quicker than it used to be. Back in the Firefox 4 days, it took about 14 CPU hours. Now it completes in about 5 hours. It may also be that Gecko is simply faster, giving less work for Valgrind to do. Given how resource-hungry Valgrind is, doing so is something of a challenge, requiring dodging timeouts, memory limits, sandboxes and proprietary driver blobs. But it is just about doable, and various bits of FirefoxOS-specific badness are in the process of getting cleaned up.
From a personal standpoint, this is very satisfying. We also have toolery TSan, Helgrind to find low level data races. Nathan Froyd and Christian Holler have made good progress in using them to detect races and in getting folks to fix those races, as tracked by metabug I look forward to the day when we can say that Mochitests is also free from detectable data races. Valgrind support on Yosemite has improved significantly in the past few months.
It is now feasible to run Firefox on Valgrind on Yosemite. Support for For various reasons, MacOS is a difficult target for Valgrind.
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Occasionally, the MacOS kernel will panic, for unknown reasons. But it does work. For serious use, you need to read that page. In the meantime, here is the minimal getting-started recipe. First, build Valgrind. Based on that analysis, I wrote a new lightweight unwinder library.
LUL — as it became known — is aimed directly at doing unwinding for profiling. Unwinding by stack scanning is also supported, although that should rarely be needed. I then start the desktop builds with the following environment variable settings:. What next for LUL? That will help to move it forward in the most immediately useful direction.
Pete Finnigan's Oracle Security Weblog
At least, if my numbers are not wrong. The title of this posting is misleading, though. I did some digging. Valgrind will, if asked nicely, print out the CFI unwinding rules as it stores them. It manages to summarise the unwind rule for each address range into just one line of text:. For libxul. That sounds like a lot of data. But those descriptions are immensely repetitive. Just how repetitive we can see by generating the descriptions, cutting off the address range part and throwing the rest through sort -u:.
A description of how to recover the return address, stack pointer and frame pointer must be pretty boring. In other words, the set of descriptions is small because GCC generates only a small set of stack frame layouts, if we restrict ourselves to considering just the parts of the frame needed for unwinding.
That has 35, address ranges containing unique descriptions. Not quite as striking as the libxul. The obvious storage optimisation is to park the 75 descriptions in a dictionary, the size of which is insignificant, and represent the , address ranges and dictionary-entry number as compactly as possible. Then, sort these entries by address range and put them in a flat array, for fast binary search. How compactly can we represent an address, length, dictionary-entry-number triple? Naively, the address is a 64 bit word, and length and entry number could be 32 bits, giving 16 bytes in total.
The address range lengths are tiny, though, mostly less than , so we could store than in a byte, and duplicate the descriptor for the occasional longer run. And the dictionary entry number in the two cases I tested would fit in 8 bits. Valgrind unwinds CFI typically 30 times faster than Breakpad. The unwinding is done using the Google Breakpad library that lives in our tree. Breakpad works well in the sense that it unwinds reliably through both libxul, system libraries and, apparently, JIT-produced code. On a low end phone, the problem is an order of magnitude worse. One thing that becomes very clear from this is that Breakpad is designed for generality, flexibility and correctness, but it is not designed for fast in-process unwinding.
Valgrind also does CFI based unwinding, and has been highly tuned over the years, particularly to support Helgrind, which is very unwind-intensive. I wondered how it compared, so I profiled it — a bit of a tricky exercise, running a big test app on an inner Valgrind which does a lot of unwinding on an outer Valgrind, running Callgrind, to get profile data.
The numbers really surprised me. Why the huge difference? Surely some mistake? I started digging. First, though, a look at the algorithm. The core algorithm is simple to understand. We start off with registers taken from the innermost frame — as a minimum, three: the program counter, the stack pointer and the frame pointer. The CFI data provides, for each possible instruction, a set of rules which say how recover the register values in the calling frame.
So we apply those rules to our three registers, and repeat.
One thing to note is that, although we only want the program counter values, we need to compute values for multiple registers, including at the very least the stack pointer. Diehard CFI-heads will recognise that the above description omits a lot of details. Nonetheless it encapsulates what the unwinder needs to be fast at. With that in mind, the differences between the Breakpad and Valgrind implementations are as follows:. Valgrind maintains a entry direct mapped cache containing previously used rule-sets. If we are doing a lot of unwinds, most of them will involve the same few hundred instructions, because the outer parts of the traces are identical or very similar.
Breakpad has no such cache. When bug lands it will have at a std::map-based cache. Breakpad has another disadvantage: its architecture forces the cache test to be placed later than is optimal. Valgrind loads the unwind rules for all shared objects at process startup. Breakpad only reads debuginfo for an object at the first visit of the object. This alone adds a couple of hundred instructions per frame. Breakpad is nothing if not flexible.
Blogging first started as a way to have an online personal web log, in which a person would journal about their day. From "web log" came the term "blog. Like most new innovations on the Internet, many entrepreneurs saw marketing potential in having a blog, and blogging took off from there. Not only can a blog be used for marketing , but also, a blog can be a home business in and of itself.
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Here's an overview of what a blog is, why it's popular, and tips to starting your own blog. Blogging is popular because it works as a marketing tool and makes money. Before starting a blog as a means to make money or to promote your existing business, you should consider these potential downsides:.
The good news is that starting a blog or adding a blog to your existing site is relativity easy and affordable. There are free blog options such as WordPress. Most webhosts offer this.
The AMP Blog – News and announcements from the AMP team
Once your blog is up and running , you need keep it active with new content to grow your business. Develop a set schedule for writing and posting blog articles. Create a content calendar so you always know what you're going to post. Like all other business ideas, your success comes from marketing, and letting your target market know about your blog.
Repurpose your blog content to promote your business, such as using quotes on Twitter or Instagram, creating a video of your article, and more. While your blog can compliment an existing business, it's also a great way to add additional income streams to your home business. You can promote other companies' products and services in affiliate marketing. You can offer advertising or feed ad networks, such as AdSense , on to your blog. If you have a service business you're promoting with your blog, you can create your own information products to compliment it.
Or, if you have your own product, you can offer a service.
By Randy Duermyer. Blogs are updated frequently. Whether it's a mommy blog in which a woman shares adventures in parenting, a food blog sharing new recipes, or a business providing updates to its services, blogs have new content added several times a week. Websites might occasionally have new information, but for the most part, they offer static, rarely changing information. Blogs allow for reader engagement. Blogs are often included in social media because of the ability for readers to comment and have a discussion with the blogger and others who read the blog.
In the past, websites had guest logs where people could say they'd visited, but a blog allows for conversation and greater interaction than a traditional website does. There are several reasons why entrepreneurs have turned to blogging:. Blogging provides an easy way to keep your customers and clients up-to-date on what's going on, let them know about new deals, and provide tips.