我正在研究 GPGPU 是否可用于加速硬件模拟。
我的推理是这样的：由于硬件本质上是非常并行的，为什么要在高度顺序的 CPU 上进行模拟？
如果不是因为其编程风格的限制性，GPU 将非常适合这一点：您运行的是单个内核，等等。
我对 GPGPU 编程经验很少，但是是否可以在 OpenCL / CUDA 中使用事件或队列？
编辑：我所说的硬件模拟并不是仿真，而是位精确的行为模拟（如 VHDL 行为模拟）。
I am investigating if GPGPUs could be used for accelerating simulation of hardware.
My reasoning is this: As hardware by nature is very parallel, why simulate on highly sequential CPUs?
GPUs would be excellent for this, if not for their restrictive style of programming: You have a single kernel running, etc.
I have little experience with GPGPU-programming, but is it possible to use events or queues in OpenCL / CUDA?
Edit: By hardware simulation I don't mean emulation, but bit-accurate behavorial simulation (as in VHDL behavioral simulation).
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我不知道有关 GPU 上的 VHDL 模拟的任何方法（或映射离散事件模拟的一般方案），但是某些应用领域通常会应用离散事件模拟，并且 >可以在GPU上有效地进行模拟（例如交通网络，如本文或这个，或化学系统的随机模拟，如本文）。
是否有可能以一种使离散时间步进模拟器可行的方式重新表述问题？在这种情况下，GPU 上的模拟应该更简单（并且仍然更快，即使看起来很浪费，因为时间步长必须足够小 - 请参阅 这篇关于基于 GPU 的元胞自动机模拟的论文为例）。
但请注意，这仍然很可能一个不平凡的（研究）问题，并且（尚未）没有通用方案的原因是您已经假设的：在GPU 很困难，并且由于巧妙的内存布局和特定于应用程序的优化和问题修改，GPU 上的大多数模拟方法都获得了加速。
I am not aware of any approaches regarding VHDL simulation on GPUs (or a general scheme to map discrete-event simulations), but there are certain application areas where discrete-event simulation is typically applied and which can be simulated efficiently on GPUs (e.g. transportation networks, as in this paper or this one, or stochastic simulation of chemical systems, as done in this paper).
Is it possible to re-formulate the problem in a way that makes a discrete time-stepped simulator feasible? In this case, simulation on a GPU should be much simpler (and still faster, even if it seems wasteful because the time steps have to be sufficiently small - see this paper on the GPU-based simulation of cellular automata, for example).
Note, however, that this is still most likely a non-trivial (research) problem, and the reason why there is no general scheme (yet) is what you already assumed: implementing an event queue on a GPU is difficult, and most simulation approaches on GPUs gain speed-up due to clever memory layout and application-specific optimizations and problem modifications.
这超出了我的专业领域，但似乎虽然下面的论文讨论的是门级模拟而不是行为模拟，但它可能包含一些有用的想法：
Debapriya Chatterjee、Andrew Deorio、Valeria Bertacco。
使用 GPU 计算进行门级仿真
http://web.eecs.umich.edu/~valeria/research /publications/TODAES0611.pdf
This is outside my area of expertise, but it seems that while the following paper discusses gate-level simulation rather than behavioral simulation, it may contain some useful ideas:
Debapriya Chatterjee, Andrew Deorio, Valeria Bertacco.
Gate-Level Simulation with GPU Computing
http://web.eecs.umich.edu/~valeria/research/publications/TODAES0611.pdf