What you say appears to be true, yet quite misleading since there's a simple process, provided by Google and built into the device, for flashing the BIOS (or at least there was on the Series 5 Chromebooks):
a patchset for non-Intel CPU support was around for years, and you guys completely ignored its existence. As well as all the critics towards non-word-aligned data structures and in-memory presentation.
how about redesigning the whole product with all that cash?
David, you're also isolated from silly talks and silly pop music which most of us have to hear around. Also I have no doubt in your career, as you have to be stronger than others to compensate for your challenge.
just trying to find positive sides in your uneasy life :)
I also bookmarked your github page, as there may be some funded work popping up in some future
yea and especially because we are closer to the hardware we can use the special ARM neon instruction set, at least i think the AMLogic chip supports them
as far as i know in about a month or so, there is a post Aaron did on his blog that said i think early may or something like that, but dont quote me on that, it was with the post about the preorder distribution
I'm using timeanddate.com since long time, and plan to continue to do so:)
Love their multi-zone meeting planner. Also lots of useful details, such as when the next daylight savings shift is going to happen in a particular location.
We use many lock-free datastructures which rely heavily upon the x86_64 architecture. Further, the expanded virtual address space enables us to mmap everything.
All network traffic is packed, and in network byte order.
> the expanded virtual address space enables us to mmap everything
This sounds like a disaster waiting to happen when a node's working set is larger than RAM. Have you considered the impact on performance due to excessive I/O resulting from this kind of overcommit?
Redis performance, for example, suffers tremendously when its database size exceeds the available RAM, which is why the authors advise implementors to cap its database size. The only real difference I can see here is that Redis allocates its storage anonymously, while Hyperdex uses file-backed pages; in either case, performance under memory pressure will largely be governed by how the VM chooses to move pages in and out of its backing store -- behavior the application has no control over.
Optimizing the performance of working sets larger than RAM is hard. Redis had a (thankfully) aborted attempt to do so (the VM is gone in the most recent stable release); and the InnoDB buffer pool in MySQL has been refined for many years and is subject to quite a bit of tuning for specific workloads. (See also http://blog.kennejima.com/post/1226487020/thoughts-on-redis for some thoughtful discussion on the subject.)
You claim on the one hand that Hyperdex was designed to work on data sets larger than RAM, but on the other hand you admit that all the benchmarks were performed with a working set smaller than RAM. I'd like to see comparative benchmarks where the working set is larger than RAM to be convinced.
Well, define "disaster". A memory-mapped dataset will cause pathological performance only when there is thrashing: If you are accessing a small percentage of the entire dataset, then unused data will be paged out and remain paged out. If the bulk of data being accessed exceeds the amount of available physical RAM, then you will get I/O trashing.
Memory-mapping is a good alternative to static allocation or a home-grown paging system because it lets the kernel handle the dynamics of allocation, letting your application transparently and gracefully handle RAM tension situation by relinquishing memory space to other apps. Kernels (including Linux and Windows) and CPUs are extremely efficient at paging I/O, much more efficient than a hand-written paging system because there's no need for the application's code to check whether a page is in physical memory — that's handled by the CPU itself.
Of course, any I/O incurred by a too-large dataset will drastically reduce performance compared to in-memory speed. But paging in itself does not necessarily lead to "disaster".
Working set larger than ram doesn't imply thrashing. It all depends on the page replacement algorithm employed by the OS, and the applications that are running.
