The just released version 12.08 of the Genode OS Framework comes with the ability to run Genode-based systems on ARM hardware without an underlying kernel, vastly improves the support for the NOVA hypervisor, and adds device drivers for the OMAP4 SoC. Further functional additions are a FFAT-based file system service, the port of the lighttpd web server, and support for on-target debugging via GDB.
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Fortunately, your concerns do not apply for Genode. In Genode's libc, the seek offset is not held at the file system but local to the process within the libc. The file-system interface is designed such that the seek offset is passed from the client to the file system with each individual file-system operation. The seek value as seen at libc API level is just a value stored alongside the file descriptor within the libc. Therefore, lseek is cheap. It is just a library call updating a variable without invoking a syscall.
Your example does indeed subvert the locking scheme. But as Genode does not provide fork(), it wouldn't work anyway. ;-)
Btw, if programs are executed within the Noux runtime (see [1]), lseek is actually an RPC call to the Noux server. So the pread/pwrite implementation carries an overhead compared to having pread/pwrite as first-class operations. So there is room for optimization in this case.
Given all the steps that are involved in a single read I/O operation, however, I am uncertain about the benefit of this specific optimization. To prevent falling into the premature-optimization trap, I'd first try to obtain the performance profile of a tangible workload. Another reason I'd be hesitant to introduce pread/pwrite as first-class operations into Noux is that in general, we try to design interfaces to be as orthogonal as possible. Thanks to this guideline, the Noux server is a cute little component of less then 5000 LOC. Introducing pread/pwrite in addition to read/write somehow spoils this principle and increases complexity.
Thanks for the pointer to the database engine. This might be a good starting point for a workload to be taken as reference when optimizing for performance and scalability.
Member since:
2009-05-27
You are welcome! :-)
Indeed, the code looks similar to the snippet you posted. See here:
https://github.com/genodelabs/genode/blob/master/libports/src/lib/li...
Fortunately, your concerns do not apply for Genode. In Genode's libc, the seek offset is not held at the file system but local to the process within the libc. The file-system interface is designed such that the seek offset is passed from the client to the file system with each individual file-system operation. The seek value as seen at libc API level is just a value stored alongside the file descriptor within the libc. Therefore, lseek is cheap. It is just a library call updating a variable without invoking a syscall.
Your example does indeed subvert the locking scheme. But as Genode does not provide fork(), it wouldn't work anyway. ;-)
Btw, if programs are executed within the Noux runtime (see [1]), lseek is actually an RPC call to the Noux server. So the pread/pwrite implementation carries an overhead compared to having pread/pwrite as first-class operations. So there is room for optimization in this case.
[1] http://genode.org/documentation/release-notes/11.11#OS-level_Virtua...
Given all the steps that are involved in a single read I/O operation, however, I am uncertain about the benefit of this specific optimization. To prevent falling into the premature-optimization trap, I'd first try to obtain the performance profile of a tangible workload. Another reason I'd be hesitant to introduce pread/pwrite as first-class operations into Noux is that in general, we try to design interfaces to be as orthogonal as possible. Thanks to this guideline, the Noux server is a cute little component of less then 5000 LOC. Introducing pread/pwrite in addition to read/write somehow spoils this principle and increases complexity.
Thanks for the pointer to the database engine. This might be a good starting point for a workload to be taken as reference when optimizing for performance and scalability.