It’s advanced and universal?
Easy to use and FREE??
YES!!!
Wouldn’t it be great to be able to access data easily with any of your favorite languages?
Build advanced apps and workflows?
XCOMPUTE utilizes a new strategy (originally developed by Google) to express complex data between computers / sessions as protocol buffers.
When you save or load to disc or transmit something over a network, the associative data structures present in your computer’s RAM must be flattened (aka serialized), buffered, and eventually reconstructed (aka deserialized) so that they can be transmit in linear fashion across a wire or into a storage device and back again.
There are many ways to do this, but most are not suitable to big data.
We’ve elected to use a special protoc compiler to auto-generate compatible interfaces that provides native access across many languages. They’re essentially feather-weight code headers or libraries that allow you to tie into xcompute.
They also sport speeds approaching the theoretical limits of the attached devices and channels (PCIe, etc).
Messages™ by Xplicit Computing provides standard support for:
While xcompute-server remains a proprietary centerpiece of the XC ecosystem, we’re excited to announce our plan to release our other official Apps, free & open!
This way, everyday users do not have to worry about subscription to xcompute-client. It makes collaboration that much easier.
Hosts maintain their xcompute-server subscriptions and now can invite friends and colleagues freely, and share results as they please with said Apps.
You own and control your data, while Xplicit continues to focus on providing high-quality, unified technologies.
For a technical overview, please read this below excerpt from the README provided with the Messages™ bundle:
XC MESSAGES 2019UNIVERSAL HIGH-PERFORMANCE NUMERIC SCHEMA / FORMAT
for complex systems, FEA, CFD, EDA, and computational geometry
A. INTRODUCTION
These proto files provide direct access to xcompute messages (file and wire) by generating accessor functions for your favorite languages. This empowers xcompute users and non-users to be able to directly manipulate and access the setup and data to/from xcompute — in a high-performance universal way — suitable for integration with other applications and scripts. Theses four files are central to the xc ecosystem (e.g. both open and proprietary apps), organized as follows:
This protocol buffer format can deliever high-performance, elegant, and flexible numerical messaging across many domains of science and engineering. (e.g. single- and double-precision floating point data, etc)
Benefits:
Limitations:
Large systems should be decomposed into several smaller systems if possible — for many reasons. It’s more efficient and accurate to specialize the physics, mediating across regions where required. Try to not solve extra DOF’s unncessarily by making one huge domain that solves everything. Memory requirements vary across methods, but is generally limited by your compute device memory…not the storage format or SSD. It is up to each workgroup to determine what is an appropriate resolution for each study. A top-down systems approach is the best way to resolve from low to high fidelity and maintain accountability across the team…
B. USING XCOMPUTE BINDINGS FOR YOUR PROJECT
Auto-generated bindings are provided for the following langauages: C++, Obj-C, C#, Python, Java, Javascript, Ruby, and Go. In your language environment, import the relevant files as headers or libraries. Statically-compiled languages such as C++, Obj-C, and C# may require linking to static library libxcmessages.a .
In your environment, various classes should become available. In C++ they can be found under the namespace Messages:: . Refer to the *.proto definitions for how each attribute is defined, knowing that your access pattern is built from these assignments directly. You can access this associative database using getters and setters…
In C++, the pattern for accessing primitives (bool, int32, int64, float, double, string) looks like:
msg.set_something(value)
auto some_value = msg.something();
Repeated fields (vectors, etc) can be accessed somewhat normally. Range-based iteration:
for (auto entry : msg.vector() )
something = entry;
Or alternatively for parallel iteration:
auto N = msg.vector_size();
something.resize(N);
#pragma omp parallel for
for (auto n=0; n'<'N; n++)
something[n] = msg.vector(n);
More complex data structures may require mutable getters:
auto N = other.vector_size();
//get a reference to mutable object
auto& vec = *msg.mutable_vector();
vec.resize(N);
#pragma omp parallel for
for (auto n=0; n<'N'; n++)
vec[n] = other.vector(n);
Please refer to the Proto3 Tutorials for typical programming patterns.
C. BUILDING YOUR OWN BINDINGS
If you’re an advanced application programmer, you may wish to build upon our bindings to customize against your own projects. This is encouraged as long as existing definitions are not altered. Use a Google Protobuf 3 Compiler to generate your new bindings. A protoc 3 compiler may be readily available in a package manager or installed from online sources or binaries. Proto2 will not work, must be Proto3+.
After protoc is installed, make a directory for each language and run the compiler from shell or script:
> mkdir -p cpp python java javascript ruby objc csharp go
> protoc --cpp_out=cpp --python_out=python --java_out=java --js_out=javascript --ruby_out=ruby --objc_out=objc --csharp_out=csharp vector.proto system.proto spatial.proto meta.proto
