Getting F# running on Linux

Getting F# running on Linux took a lot more effort than I anticipated. I am documenting the process here in the hope it may benefit someone (maybe myself) in the future. For reference, I am using OpenSuse 13.1.

  • F# is not compatible with all versions of Mono. For example, my distro repos have Mono 3.0.6 which appears to have some issues with F#. Instead, I found some people make new Mono packages available for various distros using Opensuse Build Service (OBS). For example, check out tpokorra repos for various distros such as OpenSUSE, CentOS,  Debian etc. I installed “mono-opt” and related packages. It installed mono 3.4 into /opt/mono directory.
  • If you install mono into /opt/mono, then ensure that you do append “/opt/mono/lib” into the LD_LIBRARY_PATH environment variable and /opt/mono/bin to the PATH variable. I did this in my .bashrc.
  • By default, /opt/mono/bin/mono turned out to be a symlink to /opt/mono/bin/mono-sgen. Now it appears that Mono has two versions: one using sgen GC and one using Boehm GC. I have had trouble with compilng F# using mono-sgen so I removed /opt/mono/bin/mono and then created it as a symlink to /opt/mono/bin/mono-boehm.
  • Now open up a new shell. In this shell, set up a few environment variables temporarily required for building F#. First, “export PKG_CONFIG_PATH=/opt/mono/lib/pkgconfig”. Next, we need to setup some GC parameters for Mono. It turns out compiling F# requires a lot of memory and Mono craps out with default GC parameters. I have a lot of memory in my laptop, so I set the Mono GC to use upto 2GB as follows: “export MONO_GC_PARAMS=max-heap-params=2G”. These two settings likely won’t be required after you have compiled and installed F#.
  • Now you can follow the instructions given on the F# webpage.

Specifically I did

  • git clone
  • cd fsharp
  • ./autogen –prefix /opt/mono  #Keep things consistent with rest of mono install
  • make  #Takes a lot of time
  • su
  • make install
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Fashion and buzzwords in software industry

As I get somewhat older, I have come to the realization that attempting to keep track of all the buzzwords and current fashions in the software industry is counterproductive to actual work. At some point, I have to draw a line. Trends, programming styles, languages and APIs all come and go.

It is important to keep oneself updated, but like anything else, it should be done in moderation. It is also perhaps more important to get better at fundamental computer science ideas than necessarily knowing 10 APIs to do the same thing.

Anyway, I intend to a bit more selective about which technologies I learn, focusing on a few at any given point of time. The idea is not to stand still, but rather to have a focus period bigger than a goldfish. For rest of 2014, I have made a much bigger learning list about fundamental CS ideas and a much shorter list of “technologies”. I will keep posting about some of the books I am reading.

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Metal compute notes

I have been reading some Metal API documents. Some brief notes about Metal compute from the perspective of pure compute and not graphics:

Kernels:  If you know previous GPU compute APIs such as OpenCL or CUDA etc. you will be at home. You have work-items organized in work-groups. A work-group has access to upto 16kB of local memory. Items within a work-group can synchronize but different work-groups cannot synchronize. You do have atomic instructions to global and local l memory.  You don’t have function pointers and while the documentation doesn’t mention it, likely no recursion either.  There is no dynamic parallelism either. You also cannot do dynamic memory allocation inside kernels. This is all very similar to OpenCL 1.x.

Memory model:  You create buffers and kernels read/write buffers. Interestingly, you can create buffers from pre-allocated memory (i.e. from a CPU pointer) with zero copy provided the pointer is aligned to page boundary.  This makes sense because obviously on the A7, both CPU and GPU have access to same physical pool of memory.

CPU and GPU cannot simultaneously write to buffer I think. CPU only guaranteed to see updates to buffer when the GPU command completes execution and GPU only guaranteed to see CPU updates if they occur before the GPU command is “committed”. So we are far from HSA-type functionality.

Currently I am unclear about how pointers work in the API. For example, can you store a pointer value in a kernel, and then reload it in a different kernel? You can do this in CUDA and OpenCL 2.0 “coarse grained” SVM for example, but not really in OpenCL 1.x. I am thinking/speculating they don’t support such general pointer usage.

Command queues:  This is the point where I am not at all clear about things but I will describe how I think things work. You can have multiple command queues similar to multiple streams in CUDA or multiple command queues in OpenCL. Command queues contain a sequence of “command buffers” where each command buffer can actually contain multiple commands. To reduce driver overhead, you can “encode” or record commands in two different command buffers in parallel.

Command queues can be thought of as in-order but superscalar. Command buffers are ordered in the order they were encoded. However, API keeps track of resource dependencies between command buffers and if two command buffers in sequence can be issued in parallel, they may be issued in parallel. I am speculating that the “superscalar” part applies to purely compute driven scenarios, and will likely apply more to mixed scenarios where a graphics task and a compute task may be issued in parallel.

GPU-only: Currently only works on GPUs, and not say the CPU or the DSP.

Images/textures: Haven’t read this yet. TODO.

Overall, Metal is similar in functionality to OpenCL 1.x. and it is more about having niceties such as C++11 support in the kernel language (the static subset) so you can use templates, overloading, some static usage of classes etc.  Graphics programmers will also appreciate the tight  integration with the graphics pipeline. To conclude, if you have used OpenCL or CUDA, then your skills will transfer over easily to Metal. From a theory perspective it is not a revolutionary API, and does not bring any new execution or memory model niceties. It is essentially Apple’s view on the same concepts and focused on tackling of practical issues.

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Driver overhead matters more on SoCs

There has been a lot of discussion about driver overhead in graphics and compute APIs recently. A lot of it has been centred around desktop-type scenarios with discrete GPUs. But just wanted to point out that driver overhead matters more on SoCs which integrate both CPU and GPU on the same chip.

The simple reason is SoCs have a fixed total power budget and modern SoCs dynamically distribute power budget between CPU and GPU. If there is a lot of driver overhead, which means CPU is doing a lot of work, then CPU eats a bigger part of the fixed power budget and thus the SoC may be forced to reduce the GPU frequency.  In addition to power, caches and memory bandwidth may also be shared.

I have done some benchmarking and tuning of OpenCL code for Intel’s Core chipsets and often getting the best performance out of the GPU required being more efficient on the CPU. I am pretty sure similar strategy is applicable on smartphone SoCs with the added constraint that smartphone CPUs are usually wimpy due to power constraints.


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apitest results on AMD comparing various OpenGL and D3D11 approaches

UPDATE: Issues related to texture arrays appears to be an application error. Michael Marks provides a fork that corrects some issues.

UPDATE 2: I reran some of the Linux benchmarks, earlier Linux results appear to have a bug. Performance on Linux and Windows now similar.

The strengths and weaknesses of OpenGL compared to other APIs (such as D3D11, D3D12 and Mantle) and the recent talk Approaching Zero Driver Overhead (AZDO) have become topics of hot discussion.  AZDO talk included a nice tool called “apitest” that allows us to compares a number of solutions in OpenGL and D3D. Hard data is always better than hand-wavy arguments. In the AZDO talk, data from “apitest” was shown for Nvidia hardware but no numbers were given for either Intel or AMD hardware. Michael Marks ran the tool on Linux and had some interesting results to report that imply that AMD’s driver have higher overhead than Nvidia’s driver.

However, I wanted to answer slightly different questions. For example, if we just restrict to AMD hardware, how does the performance compare to D3D? What is the performance and compatibility difference between Windows and Linux? And what is the performance of various approaches across hardware generations? With these questions in mind, I built and ran the apitool on some AMD hardware on both Linux and Windows.

Hardware: AMD A10-5750M APU with 8650G graphics (VLIW4) + 8750M (GCN) switchable graphics. Catalyst 14.4 installed on both Linux and Windows. Catalyst allows explicit selection of graphics processor.  Laptop has a 1366×768 screen.

Build: On Windows, built for Win32 (i.e. 32-bit) using VS 2012 Express and DX SDK (June 2010). Release setting was used.  On Linux, built for 64-bit using G++ 4.8 on OpenSUSE 13.1.  Required one patch in SDL cmake file.

Run: Tool was run using “apitest.exe -a oglcore -b -t 15” which is the same setting as Michael Marks. On Linux, it was run under KDE and desktop effects were kept disabled in case that makes a difference.

Issues encountered:

I encountered some issues. I am not sure if the error is in the application, the user (i.e me) or the driver.

  1. Solutions using shader draw parameters (often abbreviated as SDP in the talk) appear to lead to driver hangs on GCN and are unsupported on VLIW4. Therefore I have not reported any  SDP results here. Michael Marks also saw the same driver hangs on GCN on Linux, did some investigation and has posted some discussion here.
  2. Solutions involving ARB_shader_image_load_store (which is core in OpenGL 4.2 and not some arcane extension) appear to be broken on Windows but are working on Linux despite installing the same Catalyst version.  On Windows, the driver appears to be reporting some compilation error for some shaders saying that “readonly” is not supported unless you enable the extension. .UPDATE: Was application bug.
  3. GCN based 8750M should support bindless textures. However, some of the bindless based solutions failed to work. For example GLBindlessMultiDraw failed.  Sparse bindless also failed to work.

I did not test 8750M on Linux, partially because I am lazy and partially because I did not want to disturb my Linux setup which I use for my university work. Anyway, here is the data for 3 problems covered by apitest.

Dynamic streaming

Solution 8650G Windows (FPS) 8650G Linux (FPS) 8750M Windows (FPS)
D3D11MapNoOverwrite 14.629 0 19.6
D3D11UpdateSubresource 0.978 0 1.198
GLMapPersistent 19.987 19.471 20.885
GLBufferSubData 0.89 1.015 0.843
GLMapUnsynchronized 0.397 0.409 0.362

Textured quads


Solution 8650G Windows (FPS) 8650G Linux (FPS) 8750M Windows (FPS)
D3D11Naive 65.11 0 42.463
GLTextureArrayMultiDraw-NoSDP 346 400.25 492
GLTextureArray 235 276.67 350
GLNoTex 215.94 275.57 347.608
GLTextureArrayMultiDrawBuffer-NoSDP 212 239 472
GLNoTexUniform 81.429 93.38 133.115
GLTextureArrayUniform 80.75 92.72 109.5
GLNaiveUniform 32.717 31.64 32.059
GLNaive 27.3 15.02 27.21

Untextured quads:

Solution 8650G Windows (FPS) 8650G Linux (FPS) 8750M Windows (FPS)
D3D11Naive 4.078 0 2.16
GLMultiDraw-NoSDP 17.221 17.661 19.93
GLMapPersistent 10.844 11.089 13.687
GLDrawLoop 10.615 10.45 13.59
GLBufferStorage-NoSDP 9.862 10.041 5.096
GLMultiDrawBuffer-NoSDP 9.069 9.404 7.703
GLMapUnsynchronized 5.908 5.726 7.281
GLTexCoord 5.702 5.382 4.963
GLUniform 3.282 3.53 4.399
GLBufferRange 3.031 3.509 3.119
GLDynamicBuffer 0.361 0.515 0.37


  1. The theoretical principles discussed in the AZDO talk appear to be sound. The “modern GL” techniques discussed do appear to substantially reduce driver overhead compared to older GL techniques. The reduction was seen on AMD hardware on both Windows and Linux and worked on two different architectures (VLIW4 based APU, GCN based discrete). In particular, persistent buffer mapping (sometimes called PBM) and multi-draw-indirect (MDI) based techniques seem useful.
  2. On Windows,  the best OpenGL solutions do appear to significantly outperform D3D. I am not an expert on D3D so I am not sure if better D3D11 solutions exist.
  3. If a test ran successfully on both Windows and Linux, then the performance was qualitatively similar in most cases.
  4. However, while theoretically things look good, in practice some issues were encountered. Some of the solutions failed to execute despite theoretically being supported by the hardware.  In particular, shader draw parameters as well as some variations of bindless textures appear to be problematic. I am not sure if it was the fault of the application, the user (me) or the driver.
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OpenGL, OpenGL ES compute shaders and OpenCL spec minimums

Well, I really got this one wrong. Previously I had (mistakenly) claimed that OpenGL compute shader, OpenGL ES compute shader etc. don’t really have specified minimums for some things but I got that completely wrong. I guess I need to be more careful while reading these specs as the required minimums are not necessarily located at the same place where they are being explained.  Some of the OpenCL claims still stand though OpenCL’s relaxed specs are a bit more understandable given that it has to run on more hardware than others.

Here are the minimums:

  • OpenGL 4.3: 1024 work-items in a group, and 32kB of local memory
  • OpenGL ES 3.1: 128 work-items in a group, and 16kB of local memory (updated from 32kB, Khronos “fixed” the spec)
  • OpenCL 1.2:  1 work-item in a group, 32kB of local memory
  • OpenCL 1.2 embedded: 1 work-item in a group, 1kB of local memory
  • DirectCompute 11 (for reference): 1024 work-items in a group, 32kB of local memory

Thanks to Graham Sellers and Daniel Koch on twitter for pointing out the error. I guess I got schooled today.

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