ArrayFire VS C++ Actor Framework

Loads of people have stated why easy GPU interfaces are difficult to create, but we solve many difficult things all the time.

Ultimately I think CPUs are just satisfactory for the vast vast majority of workloads. Servers rarely come with any GPUs to speak of. The ecosystem around GPUs is unattractive. CPUs have SIMD instructions that can help. There are so many reasons not to use GPUs. By the time anyone seriously considers using GPUs they're, in my imagination, typically seriously starved for performance, and looking to control as much of the execution details as possible. GPU programmers don't want an automagic solution.

So I think the demand for easy GPU interfaces is just very weak, and therefore no effort has taken off. The amount of work needed to make it as easy to use as CPUs is massive, and the only reason anyone would even attempt to take this on is to lock you in to expensive hardware (see CUDA).

For a practical suggestion, have you taken a look at https://arrayfire.com/ ? It can run on both CUDA and OpenCL, and it has C++, Rust and Python bindings.

Low-overhead — not our goal, but Flashlight is on par with or outperforming most other ML/DL frameworks with its ArrayFire reference tensor implementation, especially on nonstandard setups where framework overhead matters

Looking for frameworks with Julia + OpenCL I found array fire. It seems quite good, bonus points for rust bindings. I will keep looking for more, Julia completely fell off my radar.

CAF

I'd like to mention the native actor model implementation CAF, the C++ Actor Framework, and share some experiences. (Disclaimer: I've been developing on CAF in the past and have a good relationship with the creator.) CAF (1) provides native actors without an VM layer, (2) type-safe interfaces so that the compiler yells at you when a receiver cannot handle a message, and (3) transparent copy-on-write messaging so that you can still push stuff through pipelines and induce only copies only when a ref count is greater than one.

In our telemetry engine VAST, we've been using CAF successfully for several years for building a distributed system that always has a saturated write path. CAF provides a credit-based streaming abstraction as well, so that you can have backpressure across a chain of actors, making burst-induced OOM issues a blast from the past. You also get all the other benefits of actors, like linking and monitoring, to achieve well-defined failure semantics: either be up and running or collectively fail, but still allowing for local recovery—except for segfaults, this is where "native" has a disadvantage over VM-based actor models.

With CAF's network transparent runtime, a message ender doesn't need to know where receiver lives; the runtime either passes the message as COW pointer to the receiver or serializes it transparently. Other actor model runtimes support that as well, but I'm mentioning it because our experience showed that this is great value: we can can slice and dice our actors based on the deployment target, e.g., execute the application in one single process (e.g., for a beefy box) or wrap actors into single OS processes (e.g., when deploying on container auto-scalers).

The deep integration with the C++ type system allowed us to define very stable RPC-like interfaces. We're currently designing a pub/sub layer as alternate access path, because users are interested in tapping into streaming feeds selectively. This is not easy, because request-response and pub/sub are two ends of a spectrum, but it turns out we can support nicely with CAF.

Resources:

- CAF: https://github.com/actor-framework/actor-framework

- VAST: https://tenzir.github.io/vast/docs/understand-vast/actor-mod... (sorry for the incompleteness, we're in migration mode from the old docs, but this page is summarizing the benefits of CAF for us best)

- Good general actor model background: http://dist-prog-book.com/chapter/3/message-passing.html#why...

VAST is a flight recorder and security content execution engine. On the one hand, there exists a continuous stream of high-volume data sources (such as network telemetry as NetFlow, Zeek, Suricata, and endpoint telemetry). On the other hand, VAST processes needle-in-haystack queries to provide answers to questions like "has this threat been relevant to us 8 months ago?", and supports threat hunters with an interactive query capability to explore the data. From an engineering perspective, we focus especially on the separation of read and write path, concurrent message passing in an actor model runtime (CAF), and leveraging open standards, like Apache Arrow, to establish a high-bandwidth data plane for sharing data with downstream tooling. A flexible plugin API enables additional security-specific use cases on top, such as realtime matching of threat intelligence or mining of asset data for passive inventorization.

Technologies: Apache Arrow, Flatbuffers, C++ Actor Framework, Linux, Docker, Kubernetes