blocks | Enable bcache or LVM on existing block devices

Note: A valid implementation for this code is to use a global array of mutexes and condition_variables. atomic variables are then mapped to these objects by their pointer via a hash function. That's why you get spurious wakeups. Some atomics share the same condition_variable.

Something like this:

Try disabling rsa-sha2-* on Paramiko side altogether:

EDIT

Following the method calls here it looks like reply_to() returns send_message(), which returns a Message object, which contains a json string set to self.json in the __init__() method. In that string you can likely find the status code (400s and 500s you can catch and deal with as you need).

Your code with 8 accumulators has enough ILP that you'd expect close to two FMA per clock, about 8 per 5 clocks (if there aren't other bottlenecks) which is a bit less than the 10/5 theoretical max.

vaddpd and vmulpd actually run on different ports on Zen2 (unlike Intel), port FP2/3 and FP0/1 respectively, so it can in theory sustain 2/clock vaddpd and vmulpd. Since the latency of the loop-carried dependency is shorter, 8 accumulators are enough to hide the vaddpd latency if scheduling doesn't let one dep chain get behind. (But at least multiplies aren't stealing cycles from it.)

Zen2's front-end is 5 instructions wide (or 6 uops if there are any multi-uop instructions), and it can decode memory-source instructions as a single uop. So it might well be doing 2/clock each multiply and add with the non-FMA version.

If you can unroll by 10 or 12, that might hide enough FMA latency and make it equal to the non-FMA version, but with less power consumption and more SMT-friendly to code running on the other logical core. (10 = 5 x 2 would be just barely enough, which means any scheduling imperfections lose progress on a dep chain which is on the critical path. See Why does mulss take only 3 cycles on Haswell, different from Agner's instruction tables? (Unrolling FP loops with multiple accumulators) for some testing on Intel.)

(By comparison, Intel Skylake runs vaddpd/vmulpd on the same ports with the same latency as vfma...pd, all with 4c latency, 0.5c throughput.)

I didn't look at your code super carefully, but 10 YMM vectors might be a tradeoff between touching two pairs of cache lines vs. touching 5 total lines, which might be worse if a spatial prefetcher tries to complete an aligned pair. Or might be fine. 12 YMM vectors would be three pairs, which should be fine.

Depending on matrix size, out-of-order exec may be able to overlap inner loop dep chains between separate iterations of the outer loop, especially if the loop exit condition can execute sooner and resolve the mispredict (if there is one) while FP work is still in flight. That's an advantage to having fewer total uops for the same work, favouring FMA.

On top of that, one of the primary reason behind their use (in my experience, at least) is to limit or outright eliminate memory fragmentation for long-running processes in memory-constrained scenarios. So it is sort of assumed that "throwing more memory at the problem" is going to be counterproductive more often than not.

Because of this, there is no universal one-size-fit-all rule here. What is preferable will invariably be dictated by the needs of your application.

Figuring out the correct values for max_blocks_per_chunk and largest_required_pool_block is ideally based on a thorough memory usage analysis so that the achieved balance benefits the application as much as possible.

However, given the wording of the question:

How to avoid unnecessary memory allocations?

How to reduce the number of allocation that take place as much as possible?

If you want to minimize upstream allocator calls as much as possible, then it's simple:

• Make largest_required_pool_block the largest frequent allocation size you expect the allocator to face. Larger blocks means more allocations will qualify for pooled allocation.
• Make max_blocks_per_chunk as large as you dare, up to the maximum number of concurrent allocations for any given block size. More blocks per chunks means more allocations between requests to the upstream.

The only limiting factor is how much memory footprint bloat you are willing to tolerate for your application.