The usual composition will be of articles on specific topics and gradually increasing complexity.

This post will include an additional Table of Contents for published articles as we go.

Table of Contents

1. Preface

For any question or comment, please contact us through our email address: support (at) hoopoe-cloud.com.

Following last year success, the event will cover recent developments and progress in cloud technologies. Presenting with top-of-the-line companies active in this field, including (partial list): Amazon, Google, eBay, IBM, HP, Sun, RedHat and more.

Additional “hands-on” labs and workshops are offered during the event for participants that would like to learn more about cloud technologies and integration possibilities.

We are also presenting Hoopoe at the summit, for GPU Cloud Computing, and providing a workshop on GPU Computing in general and Hoopoe as well.

This event ends 2009 and symbolically the last decade, marking cloud computing as a major development that we are about to see more and more in the next years.

You are invited to join us during the event.
Agenda
Registration

We would like to announce for the release of CUDA.NET 2.3.7.

This version addresses various issues with runtime API and types. The change was in data types and structures compliance with the native wrapper of CUDA Runtime API, to support cross-platform environments operating in 32 or 64 bit mode. The structures now support the SizeT structure we introduced in the previous CUDA.NET release.

Link to the download page.

Please send us your comments and feedback.

We are happy to announce the immediate availability of OpenCL.NET for the public.
This library provides a .NET implementation and wrapping of the OpenCL interface for GPU computing (and general computing as well).

Currently, the library supports revision 1.0.43 of Khronos (being the latest version of the standard).

Users may test the library with NVIDIA released drivers for OpenCL, or on other architectures as OpenCL should be supported on (Intel, AMD CPU etc.).

The API in this release was adapted to be cross platform in mind, and code, using the new SizeT construct for transparent handling of 32/64 bit platforms.

In addition, there is only one version of the library conforming to all operating systems who support OpenCL, regardless of Windows, Linux or Mac.

For any question, request, bug report or else, please contact us at: support@hoopoe-cloud.com.

We hope you will find this library useful.

In this post I wanted to introduce you with a new construct we added to the latest release of CUDA.NET (2.3.6) and will be available with the published OpenCL.NET library.

The problem

.NET is a very fixed environment, defining well known types, such that an int is always 4 bytes long (32 bit) and a long is always 8 bytes long (64 bit).

This is not the case with native code, for developers of C/C++. Writing a program in 32 bit environment, will always yield 32 bit types, unless using specific directives to get 64 bit variables. When writing 64 bit programs, they do get access to 64 bit wide variables as primitives supported by the compiler.

This clearly creates a portability problem for code and applications written in 32 and 64 bit environments.

Another example, is pointer size, where in C/C++ environments, under 32 bit the pointer is 4 bytes wide (int) and under 64 bit systems it is 8 bytes wide (long). The .NET environment (through different languages) provides a simple construct to overcome this problem, namely the IntPtr object, which some of you may be familiar with.

Now, coming back to our domain, the runtime API (also the driver in a new CUDA 2.3 function) and OpenCL makes extensive use of the C/C++ size_t data type. This data type ensures for developers that under different environments they will get the maximum width of the supported data type, unsigned int for 32 bit systems and unsigned long for 64 bit systems.

Possible options

By means of the interoprating library (wrapper), such as CUDA.NET, it creates a problem, since the API should provide several versions of the function, one given an uint (to map to 32 bit with unsigned int C/C++ type), and ulong (to map against unsigned long in 64 bit C/C++ systems). Supplying such an interface to the user will have to force him a specific behavior and system, since in .NET, the uint is always 32 bit wide, and ulong is 64 bit wide, no matter what.

Another option can be to provide a unique, standalone interface, using the IntPtr object, since .NET takes care to make it 32 bit wide in 32 bit systems and 64 bit wide for 64 bit systems, dynamically, without user intervention.

But using the IntPtr and a very serious downside, it is not dynamic, once it’s value is set, it cannot be changed through simple arithmetic operators, like +,-,*,/ or else.

The solution

Exactly for this purpose we created the SizeT object (structure). First, it maps to the same name as it’s native counterpart (size_t) and second it provides the dynamic mechanisms we want for working with 32 or 64 bit systems transparently.

SizeT can serve just like any other basic primitive in .NET.
For example:

SizeT temp = 15;
uint value = (uint)temp;
ulong value2 = (ulong)temp;
temp = value;


Internally, the SizeT wraps the IntPtr object to provide the same dynamic capabilities under 32 and 64 bit platforms.
It can host the required .NET primitives (int, uint, long, ulong), so when programming, one will make a good habit for using the SizeT instead of other data types (working with the runtime CUDA API).

For OpenCL the interface was built from the first place to use SizeT in mind, as the OpenCL API uses only size_t data types for cross platform functions.

Consider a basic data type of float, the corresponding array is declared as: float[], in C# or otherwise in different languages, but the principle is the same. In addition to these primitives (byte, short, int, long, float, double) there is also support for vector data types that CUDA support, such as Float2, where it is composed of 2 consequtive float elements.

What happens when you want to pass more complex data types that are not supported by CUDA.NET?

In this case, there are several techniques to achieve this goal, some maybe more complex to empploy than others, and it mostly depend on your expected usage.

1. Declaring a new copy function

Well, that’s always an option if you wish to extend the API of functions. In such case, the developer declares a new copy function to use, with expected parameters and consumes it.

The following example can show a little more:

// This is a dummy, complex data type
struct Test
{
public int value1;
public float value2;
}

// Define a new copy function to use with CUDA, assuming running under Linux
[DllImport("cuda")]
public static extern CUResult cuMemcpyHtoD(CUdeviceptr dst, Test[] src, uint bytes);

The definition above is for a function, to use, capable of copying data from an array of Test objects to device memory.
But, it may not always be convenient.

2. The dynamic, simpler way

Well, .NET offers one more possibility to convert .NET objects into native representation, without using “unsafe” mechanisms.

For this purpose, there is an object called “GCHandle” to use. This object provides an advanced control over the garbage collector of .NET to lock objects in memory and get their native pointer (IntPtr in .NET).

Since all copy functions in CUDA.NET support the IntPtr data type, one can use this mechanism as a generic way to copy data to the GPU. In practice, when a user calls one of the existing copy functions, the exact process is performed.

Again, consider the Test structure we created before.

// Getting native handle from an array
Test[] data = new Test[100];
// Fill in the array values...
GCHandle ptr = GCHandle.Alloc(data, GCHandleType.Pinned);
IntPtr src = ptr.AddrOfPinnedObject();
// Now copy to the GPU memory from this pointer...
....
// When finished, don't forget to free the GCHandle!
ptr.Free();

This is a simple process for exposing complex .NET data types to CUDA and CUDA.NET to be processed by the GPU.

In the next article we will present the new SizeT object we added for portability between 32 and 64 bit systems.

We’ve just released the latest version (2.3.6) of CUDA.NET library.

Beside supporting the latest features of CUDA 2.3 (double precision FFT, advanced memory allocation and more) we added more features to the API of the runtime and graphics (DX/GL) to better support 32/64 systems and be portable.

Following this article we will publish a few series of articles presenting the new constructus we added, and native interoperability, which is always an issue with .NET code and advanced demands for applications. They intend to show how to create portable code between systems and using complex structures an data types passed to the GPU.

Enjoy!

We invite organizations, research institutes and privates to tell us about about their use of CUDA.NET for different purposes – developing a product, researching variety of scientific fields and more.

Users willing to contribute their story are invited to send their details to the following address: cuda.net@gass-ltd.co.il and we will contact them soon.

Thank you for your cooperation.

This release aligns with CUDA 2.2 API and features, and provides further improvements with CUDA.NET.
To download page.

Few of the additions/changes:

• Supporting CUDA 2.2 API (zero copy etc.)
• CUDA class supports all driver functions, adding few missing texture functions into the API
• Removing double precision FFT routines from CUFFT – the functions were there for future support, but are no longer available
• Adding MSDN/CHM based documentation for the library
• Extending the runtime API support to allow various memory copies and the latest 2.2 API

We you will all find that release useful.

You are invited to provide us comments for usage and in general about the library to improve it.
You can send all that information to cuda.net@gass-ltd.co.il.

The new release of CUDA.NET, provides support for new DirectX 10 API interoperability, and JIT compiler.

To download click here.

DirectX 10 interoperability

The new API by NVIDIA allows to integrate existing DirectX 10 applications with CUDA, to provide another level of computing, if for post-processing, image processing or other computations to perform.

DirectX 9 API is still supported.

JIT Compiler

A new compiler support is provided by NVIDIA, through the API. This allows to generate CUDA kernel code in runtime and compile it on demand using this new facility.

In addition, it allows to attach kernel source code to an application, and compile it at the site, using specific configuration: maximum register usage, specific hardware support and more.

FIXes

This release of CUDA.NET 2.1, fixes an issue with CUDAExecution class. When running a computation on the GPU using the class, an then calling the Clear method, didn’t clear the parameters state. As with this release the issue was fixed.