Sony ships OpenCL on Xperia handsets

Sony published a blog post about OpenCL being available on Xperia devices such as Xperia Z, ZL, ZR, Z Ultra, Z1 and Tablet Z. These are Snapdragon S4 Pro and Snapdragon 800 devices and according to Sony come with OpenCL drivers for the Adreno GPUs. They also indicate that they intend to continue to support OpenCL. Very good news.

Renderscript in Android 4.4

Android 4.4 has been announced and brings an interesting new feature: Renderscript Compute is now accessible from the NDK from C++ code. There is very little documentation atm, or at least I haven’t found it but you should download NDK r9b at the time of writing to see a HelloComputeNDK sample in the samples folder. It looks like a fully native solution, i.e. you do not need to write any Java code to access it afaik. The Android 4.4 release notes say that you should be able to access all renderscript functionality including intrinsics and user-defined kernels from the NDK.  This is quite a nice development and kudos to the Renderscript team, but they do desperately need to address other concerns such as documentation. I still do think that they need to provide access to a lower-level API, such as OpenCL, to complement the high-level Renderscript. But I guess such is life.

As an aside, release notes also say that Android 4.4 enables GPU acceleration of Renderscript (I am assuming only for Filterscript) for all recent Nexus devices (Nexus 4, Nexus 5, Nexus 10, Nexus 7 2013) with the exception of Tegra 3 powered Nexus 7 2012, which has an ancient GPU architecture.

Qt on Android and BB10 experience

Finally I have managed to port a small desktop app to Android and Blackberry 10. The app was based on QWidgets and was based on Qt 4.8.  The app was not for open distribution unfortunately so I can’t share the app. However, I can share some experiences:

Blackberry 10:  I just changed to the Blackberry-provided qmake, ran qmake and make, and the app compiled without any changes for Blackberry 10. However, packaging the app requires a few additional steps. You have to obtain signing keys (which I already had), provide a bar-descriptor.xml and then run the blackberry-nativepackager command. The process is generally well-documented and went fine. I was able to test on a Blackberry Z10. I initially ran into an issue that I had forgotten to specify the proper permissions in the bar-descriptor for accessing files, but once I corrected it things ran “ok”. Most of the widgets actually rendered fine and layout scheme worked as expected. However, things were not perfect either. For example, the file picker dialog was not very nice but at least the app was servicable. The lack of integration with BB10 UI conventions is an issue though so will have to see if there is a way to integrate stuff like swipe-down menus from a pure QWidget (or perhaps Qt Quick 1/2) based app without using Cascades.

Android:  For Android, I used Android 5.1 and actually used Qt Creator 2.8.1 to set everything up. Changing to Qt 5.1 required very minimal changes such as a couple of ifdefs for changed header file names. Qt Creator asked for Android SDK and NDK paths, and after that setup most of the things automatically including inbuilt simulator support. If you connect a device over adb, those are recognized as well for deployment and testing.  Qt Creator was automatically generating unsigned APKs but doesn’t seem to generate signed apk’s, or at least I couldn’t figure out how.
Overall, things were going smoothly on Android .. until I actually ran the app. Turns out QWIdgets look really ugly on Android and some of the layouting doesn’t really work the way you would want. Some widgets, like QComboBox used for implementing drop-down menus, did not render correctly while some others like file dialogs were ugly and almost unusable. After a few careful changes, I was able to make it work but still integration was poor.

I would say, at least on Android, you should try to avoid QWidgets and start to use QML. QML will allow you far more control over the look, feel and layout of your app. I do hope that something like Qt Quick Controls come to Android because raw QML is a bit too much DIY. The other issue is that, I am still not clear on how Qt based apps can integrate with Android UI guidelines such as integration with the back button.  I am also not clear how you can integrate, say, invoking the email application or the web browser. Need to figure these out next.




AMD VLIW4 GPUs and double precision floating point (fp64)

UPDATE: I think the 6900 series support for double-precision seems fine, but Trinity/Richland remains unexplained.

AMD has two VLIW4 based parts: Radeon 6900 series and Trinity/Richland APU’s GPU products (Radeon 7660D, 8650G etc.). Some of the launch media coverage stated that these GPUs have fp64 capability. I recently got a Richland based system to work on and realized the following:

a) AMD does not support cl_khr_fp64 (i.e. the standard OpenCL extension for fp64) on the 8650G GPU and only supports cl_amd_fp64. But AMD’s documentation is not very clear about the difference.

b) Earlier driver versions for Trinity (which afaik has the same silicon as Richland) definitely had cl_khr_fp64 support, but it was later removed and demoted to only cl_amd_fp64.

c) Richland’s GPU (8650G) does not seem to support double precision under Direct3D either.

d) Forum postings indicate latest drivers for 6900 series GPUs also do not support cl_khr_fp64, and only support cl_amd_fp64. I am not sure about the fp64 support status under DirectCompute.

My speculation is that AMD discovered some issue with IEEE compliance on fp64 units in the VLIW4 GPUs and hence AMD is unable to support APIs where full IEEE compliance is required. If anyone has any insight into the issue, then let me know.

DirectCompute from an OpenCL and CUDA perspective

Currently, most of my GPGPU experience is with OpenCL and CUDA. I have recently been looking at DirectCompute as another IHV-neutral API besides OpenCL. I have tried porting some of my OpenCL code to DirectCompute to gain experience. Here are some notes, in no particular order, from the perspective of writing compute code which has no graphics component:

1. Basic programming paradigm is similar to OpenCL 1.2 and basic CUDA. You have threads organized into thread groups, you have access to local memory/on-chip shared memory and synchronization etc is fairly similar as well.

2. However, it is far behind the functionality in CUDA 5.x and OpenCL 2.0. For example, there is no support for dynamic parallelism.  It is likely that Microsoft is considering adding these features, but with no public roadmap it is difficult to say anything. DirectCompute has not really evolved much since it started shipping in Windows 7 in late 2009 (i.e. almost 4 years ago).

3. No support for multiple command queues per context. CUDA has streams and OpenCL has the ability to create multiple command queues per context, but I think there is only one implicit command queue per device context in DirectCompute.  I think this will be a problem under many compute scenarios.

4. Shared memory support is very limited. D3D 11.2 introduces some features that take one step towards shared memory, but it is not fully there yet. On OpenCL, we already have decent shared memory support under OpenCL 1.2 on Intel platforms. OpenCL 2.0 is going to bring proper shared memory support on many platforms.

5. Double-precision support in HLSL is limited. There are no trigonometric functions or exponential functions. On Windows 7, you don’t even get double-precision FMA or divide in the shader bytecode. You can potentially the missing functions yourself but a serious compute API should include them. Using Microsoft’s C++ AMP instead of using DirectCompute takes care of some of this on Windows 8.

6. Vendor tools are geared for games and graphics applications. Profilers from various vendors all provide “per frame” analysis, which is useful for graphics applications but useless for pure compute scenarios. OpenCL and CUDA tools are geared for compute and are getting pretty good. I think this will again be different for C++ AMP.

7. Driver quality for DirectCompute is far more consistent across vendors compared to OpenCL. With OpenCL, it is not uncommon to run into frustrating bugs in various drivers. Also, sometimes driver writers interpret the OpenCL spec quite “creatively” which is very frustrating and often requires multiple codepaths even in host API code. DirectCompute drivers are far more robust, less buggy and the program behavior is usually what you expect across all vendors.

8. Hardware-vendor independant shader bytecode is great to have in DirectCompute. OpenCL SPIR will tackle this but it is not yet implemented.

9. Thread-group size is compile time constant in DirectCompute. In OpenCL and CUDA, you can delay specifying the group size until dispatch and can dispatch it with a different group size in every invocation. Even OpenGL compute shaders are getting this ability with a new extension (GL_arb_compute_variable_group_size).

10. Documentation is not that great. I guess I am used to downloading OpenCL specs directly and reading them while MSDN is a bit harder to navigate. For example, Direct3D 11.2 docs are essentially diffs over D3D 11.1 which makes it hard to get the complete up-to-date picture in one place. Vendor documentation is also woefully inadequate on many DirectCompute related things. For example, just trying to find out which GPUs from any vendor supports all double-precision instructions and which doesn’t is hard. Vendors also don’t seem to bother providing detailed optimization guides for DirectCompute.

My experience is limited however, and it is likely I have gotten some things wrong. If you have any corrections to offer, please let me know 🙂

Overall I feel that if your app is not already using Direct3D, you probably should not use DirectCompute. You are probably better off choosing OpenCL for many compute scenarios. OpenCL has some technical advantages over DirectCompute as outlined above, is a more future-proof and platform-independent path and has much better documentation and tooling support today than DirectCompute for pure compute scenarios.  Alternately, if you want to stick to Microsoft stack, then you are probably better off choosing C++ AMP over DirectCompute.