stencil | A minimalist template engine for Python3

I figured it out eventually. In short there is no automatic way to handle this. A simple implementation is to draw opaque objects first while writing to depth buffer, then draw transparents with only depth test, without writing to depth stencil. Transparents should be sorted by depth and rendered from farthest to closest to view point.

Follow up to previous images:

check this post for named slots -> How to get the slots value in a Svelte component? I hope it gives you direction..

You can try importing the css in the index.ts or index.js file that will be available in the location -> cypress/support/index.ts

The styles are .scss which need preprocessing, which happens in cypress/plugins/index.js

You already have a webpack.config in your plugins folder.

Does it have a rule for scss?

I'm not an expert in Vue but here's how I did it:

Add the following to your ./vue.config.js (or create it if it doesn't exist):

Generally speaking, "intermediate resources" (such as depth stencils and intermediate render targets) are not presented on the screen.

So when one swapchain is ready to present (and you start recording next frame commands), those resources can be safely overridden, since the content is ready within the swap chain.

The only exception for those resources are generally buffers that are uploaded from cpu, for them it is needed to have one per frame (since you will start to copy data while another command list is still executing).

First of all, the state of the OpenMP task scheduling on NUMA systems is far from being great in practice. It has been the subject of many research project in the past and they is still ongoing project working on it. Some research runtimes consider the affinity hint properly and schedule the tasks regarding the NUMA node of the in/out/inout dependencies. However, AFAIK mainstream runtimes does not do much to schedule tasks well on NUMA systems, especially if you create all the tasks from a unique NUMA node. Indeed, AFAIK GOMP (GCC) just ignore this and actually exhibit a behavior that make it inefficient on NUMA systems (eg. the creation of the tasks is temporary stopped when there are too many of them and tasks are executed on all NUMA nodes disregarding the source/target NUMA node). IOMP (Clang/ICC) takes into account locality but AFAIK in your case, the scheduling should not be great. The affinity hint for tasks is not available upstream yet. Thus, GOMP and IOMP will clearly not behave well in your case as tasks of different steps will be often distributed in a way that produces many remote NUMA node accesses that are known to be inefficient. In fact, this is critical in your case as stencils are generally memory bound.

If you work with IOMP, be aware that its task scheduler tends to execute tasks on the same NUMA node where they are created. Thus, a good solution is to create the tasks in parallel. The tasks can be created in many threads bound to NUMA nodes. The scheduler will first try to execute the tasks on the same threads. Workers on the same NUMA node will try to steal tasks of the threads in the same NUMA node, and if there not enough tasks, then from any threads. While this work stealing strategy works relatively well in practice, there is a huge catch: tasks of different parent tasks cannot share dependencies. This limitation of the current OpenMP specification is a big issue for stencil codes (at least the ones that creates tasks working on different time steps). An alternative solution is to create tasks with dependencies from one thread and create smaller tasks from these tasks but due to the often bad scheduling of the big tasks, this approach is generally inefficient in practice on NUMA systems. In practice, on mainstream runtimes, the basic statically-scheduled loops behave relatively well on NUMA system for stencil although it is clearly sub-optimal for large stencils. This is sad and I hope this situation will improve in the current decade.

Be aware that data initialization matters a lot on NUMA systems as many platform actually allocate pages on the NUMA node performing the first touch. Thus the initialization has to be parallel (otherwise all the pages could be located on the same NUMA node causing a saturation of this node during stencil steps). The default policy is not the same on all platforms and some can move pages between NUMA nodes regarding their use. You can tweak the behavior with numactl. You can also fetch very useful information from the hw-loc tool. I strongly advise you to manually the location of all OpenMP threads using OMP_PROC_BIND=True and OMP_PLACES="{0},{1},...,{n}" where the OMP_PLACES string set can be generated from hw-loc regarding the actual platform.

For more information you can read this research paper (disclaimer: I am one of the authors). You can certainly find other similar research paper on the IWOMP conference and the Super-Computing conference too. You could try to use research runtime though most of them are not designed to be used in production (eg. KOMP which is not actively developed anymore, StarPU which mainly focus on GPUs and optimizing the critical path, OmpSS which is not fully compatible with OpenMP but try to extend it, PaRSEC which is mainly designed for linear algebra applications).

In OpenMP specification you can find the corresponding definitions:

sibling tasks - Tasks that are child tasks of the same task region.

child task - A task is a child task of its generating task region. A child task region is not part of its generating task region.

task region - A region consisting of all code encountered during the execution of a task. COMMENT: A parallel region consists of one or more implicit task regions

In the description of parallel construct you can read that:

A set of implicit tasks, equal in number to the number of threads in the team, is generated by the encountering thread. The structured block of the parallel construct determines the code that will be executed in each implicit task.

This practically means that in the parallel region many task regions are generated and using #pragma omp for different task region will generate explicit tasks (i.e #pragma omp task...). However, only tasks generated by the same task region are sibling tasks (not all of them!). If you want to be sure that all generated tasks are sibling tasks, you have to use a single task region (e.g. using single construct) to generate all the explicit tasks.

Note that your code gives the correct result, because there is an implicit barrier at the end of worksharing-loop construct (#pragma omp for). To remove this barrier you have to use the nowait clause and you will see that the result will be incorrect in such a case.

Another comment is that in your case the workload is smaller than parallel overheads, so my guess is that your parallel code will be slower than the serial one.

By default the name of custom events is the property name, in your case tipInputEnterEvent. So the listener should instead be: