Hello, I want to use the cuda backend of vexcl but it doesn't work I have the following error :

Error explicit specialization of class "vex::traits::is_vector_expr_terminal<size_t, void>" must precede its first use C:\Utils\vexcl-master\vexcl\backend\cuda\texture_object.hpp 41

I have boost 1.69, visual studio 2017 15.9.7 and cuda 10. Maybe my boost version is too recent ?

It's a feature request.

Is it possible to support sparse vectors? Right now, when a (big) dense vector a + a (very) sparse vector b, the effective flops is about 60K, as many computation are wasted for + 0 (i.e. the missing elements in the sparse vector).

I have been using ODEINT for doing Monte Carlo simulations of a parachute system descending to the ground model as a point mass. I would like to utilize VexCL to leverage GPUs to speed up computation. I have mostly been able to modify your symbolic example for my particular equations of motion, but I have run into an issue. I need to interpolate a 3D wind field as a function of the position of the system in order to determine the drag forces acting on the system. I would ideally like to leverage the texture interpolation capabilities of the GPU to do this. Do you have any suggestions?

Thanks

If it helps, here is description of the system model:

Dear all,

a quick question: is it possible to tell VexCL to reuse an OpenCL context/device/queue already initialized by another library? It would be nice to have something similar to ViennaCL's viennacl::ocl::setup_context() and viennacl::ocl::switch_context(), which take already initialized cl_context and cl_device_id objects as input.

Thanks!

While trying to debug some VexCL code on the CPU, I notice that writing to a vex::multivector<> generates an exception, EXC_BAD_ACCESS (code=2).

For example, the snippet

#define VEXCL_SHOW_KERNELS
#include "vexcl/vexcl.hpp"

int main() {
   vex::Context ctx(vex::Filter::Type(CL_DEVICE_TYPE_CPU) && vex::Filter::DoublePrecision);

   vex::multivector<double, 16> u2(ctx.queue(), 5);   
   u2(0) = 0.0;
}

generates a suitable-looking kernel, but crashes with an exception when the write to u2 is performed. But running it on a GPU device works as expected. This is on OS X 10.9.

Because it works on the GPU this has the appearance of an implementation bug with the OpenCL shipped with 10.9. Unfortunately I don't have access to a different one which would enable me to check. Is there any way to debug such issues, or is the only realistic approach to report it to Apple and hope for the best?

It appears to mostly work with Apple's implementation - however utests does fail in one case:

User-defined stencil operator: ................................. failed.

I'll investigate further myself, but I just included this here for future reference.

This moves to using the CMake Boost:: targets if they are available, and making them if they are not. The problem it is fixing (-pthread no being added when it is required on some systems) should be fixed even in the case the Boost:: targets are not available (old CMake or simi-old CMake combined with newer Boost).

(The threads test case does not build on CentOS 7 system without this patch)

A bug with AMD SI cards was reported in this thread https://community.amd.com/message/2869393#comment-2869393

A solution was also proposed on that thread. If I were to implement that solution within vexcl how do I go about doing that? Essentially, with every kernel, a null kernel would need to passed. It can safely be ignored with an #ifdef declaration for other cards. I can show the results of an example that illustrate this case:

[email protected]:~/build/vexcl/examples$ ./complex_simple

1. Hainan (AMD Accelerated Parallel Processing)

X * Y = (0,16) * (16,0) = (-5.62355e+303,7.1998e-304)i X * Y = (1,15) * (15,1) = (1.76736e+186,-2.529e-186)i X * Y = (2,14) * (14,2) = (-5.43986e-256,-7.4239e-199)i X * Y = (3,13) * (13,3) = (-1.15801e-125,-1.42689e-184)i X * Y = (4,12) * (12,4) = (-1.5799e-103,-2.30511e+235)i X * Y = (5,11) * (11,5) = (2.83923e+103,-2.783e+307)i X * Y = (6,10) * (10,6) = (-4.95723e+305,1.35145e+188)i X * Y = (7,9) * (9,7) = (-2.73677e-48,-0.00275755)i X * Y = (8,8) * (8,8) = (-2.26843e-106,-1.45955e-201)i X * Y = (9,7) * (7,9) = (1.79762e+106,2.97045e+201)i X * Y = (10,6) * (6,10) = (-nan,2.14326e-308)i X * Y = (11,5) * (5,11) = (-8.49166e-200,5.26253e+199)i X * Y = (12,4) * (4,12) = (-3.88897e+306,2.78449e+188)i X * Y = (13,3) * (3,13) = (1.69952e+184,1.77589e-234)i X * Y = (14,2) * (2,14) = (-1.94762e-104,-4.35661e+232)i X * Y = (15,1) * (1,15) = (-1.82884e-128,1.00333e-232)i X / Y = (0,16) / (16,0) = (0,256) X / Y = (1,15) / (15,1) = (0,226) X / Y = (2,14) / (14,2) = (0,200) X / Y = (3,13) / (13,3) = (0,178) X / Y = (4,12) / (12,4) = (0,160) X / Y = (5,11) / (11,5) = (0,146) X / Y = (6,10) / (10,6) = (0,136) X / Y = (7,9) / (9,7) = (0,130) X / Y = (8,8) / (8,8) = (0,128) X / Y = (9,7) / (7,9) = (0,130) X / Y = (10,6) / (6,10) = (0,136) X / Y = (11,5) / (5,11) = (0,146) X / Y = (12,4) / (4,12) = (0,160) X / Y = (13,3) / (3,13) = (0,178) X / Y = (14,2) / (2,14) = (0,200) X / Y = (15,1) / (1,15) = (0,226)

If I run it the second time [email protected]:~/build/vexcl/examples$ ./complex_simple

1. Hainan (AMD Accelerated Parallel Processing)

X * Y = (0,16) * (16,0) = (0,1)i X * Y = (1,15) * (15,1) = (0.132743,0.99115)i X * Y = (2,14) * (14,2) = (0.28,0.96)i X * Y = (3,13) * (13,3) = (0.438202,0.898876)i X * Y = (4,12) * (12,4) = (0.6,0.8)i X * Y = (5,11) * (11,5) = (0.753425,0.657534)i X * Y = (6,10) * (10,6) = (0.882353,0.470588)i X * Y = (7,9) * (9,7) = (0.969231,0.246154)i X * Y = (8,8) * (8,8) = (1,0)i X * Y = (9,7) * (7,9) = (0.969231,-0.246154)i X * Y = (10,6) * (6,10) = (0.882353,-0.470588)i X * Y = (11,5) * (5,11) = (0.753425,-0.657534)i X * Y = (12,4) * (4,12) = (0.6,-0.8)i X * Y = (13,3) * (3,13) = (0.438202,-0.898876)i X * Y = (14,2) * (2,14) = (0.28,-0.96)i X * Y = (15,1) * (1,15) = (0.132743,-0.99115)i X / Y = (0,16) / (16,0) = (0,256) X / Y = (1,15) / (15,1) = (0,226) X / Y = (2,14) / (14,2) = (0,200) X / Y = (3,13) / (13,3) = (0,178) X / Y = (4,12) / (12,4) = (0,160) X / Y = (5,11) / (11,5) = (0,146) X / Y = (6,10) / (10,6) = (0,136) X / Y = (7,9) / (9,7) = (0,130) X / Y = (8,8) / (8,8) = (0,128) X / Y = (9,7) / (7,9) = (0,130) X / Y = (10,6) / (6,10) = (0,136) X / Y = (11,5) / (5,11) = (0,146) X / Y = (12,4) / (4,12) = (0,160) X / Y = (13,3) / (3,13) = (0,178) X / Y = (14,2) / (2,14) = (0,200) X / Y = (15,1) / (1,15) = (0,226)

The results look fine now. The result of clinfo is [email protected]:~/build/vexcl/examples$ clinfo Number of platforms: 1 Platform Profile: FULL_PROFILE Platform Version: OpenCL 2.1 AMD-APP (2639.3) Platform Name: AMD Accelerated Parallel Processing Platform Vendor: Advanced Micro Devices, Inc. Platform Extensions: cl_khr_icd cl_amd_event_callback cl_amd_offline_devices

Platform Name: AMD Accelerated Parallel Processing Number of devices: 1 Device Type: CL_DEVICE_TYPE_GPU Vendor ID: 1002h Board name: AMD Radeon (TM) R5 M330 Device Topology: PCI[ B#1, D#0, F#0 ] Max compute units: 5 Max work items dimensions: 3 Max work items[0]: 1024 Max work items[1]: 1024 Max work items[2]: 1024 Max work group size: 256 Preferred vector width char: 4 Preferred vector width short: 2 Preferred vector width int: 1 Preferred vector width long: 1 Preferred vector width float: 1 Preferred vector width double: 1 Native vector width char: 4 Native vector width short: 2 Native vector width int: 1 Native vector width long: 1 Native vector width float: 1 Native vector width double: 1 Max clock frequency: 750Mhz Address bits: 64 Max memory allocation: 1596905472 Image support: Yes Max number of images read arguments: 128 Max number of images write arguments: 8 Max image 2D width: 16384 Max image 2D height: 16384 Max image 3D width: 2048 Max image 3D height: 2048 Max image 3D depth: 2048 Max samplers within kernel: 16 Max size of kernel argument: 1024 Alignment (bits) of base address: 2048 Minimum alignment (bytes) for any datatype: 128 Single precision floating point capability Denorms: No Quiet NaNs: Yes Round to nearest even: Yes Round to zero: Yes Round to +ve and infinity: Yes IEEE754-2008 fused multiply-add: Yes Cache type: Read/Write Cache line size: 64 Cache size: 16384 Global memory size: 2146349056 Constant buffer size: 65536 Max number of constant args: 8 Local memory type: Scratchpad Local memory size: 32768 Max pipe arguments: 0 Max pipe active reservations: 0 Max pipe packet size: 0 Max global variable size: 0 Max global variable preferred total size: 0 Max read/write image args: 0 Max on device events: 0 Queue on device max size: 0 Max on device queues: 0 Queue on device preferred size: 0 SVM capabilities: Coarse grain buffer: No Fine grain buffer: No Fine grain system: No Atomics: No Preferred platform atomic alignment: 0 Preferred global atomic alignment: 0 Preferred local atomic alignment: 0 Kernel Preferred work group size multiple: 64 Error correction support: 0 Unified memory for Host and Device: 0 Profiling timer resolution: 1 Device endianess: Little Available: Yes Compiler available: Yes Execution capabilities: Execute OpenCL kernels: Yes Execute native function: No Queue on Host properties: Out-of-Order: No Profiling : Yes Queue on Device properties: Out-of-Order: No Profiling : No Platform ID: 0x7f5a050f49f0 Name: Hainan Vendor: Advanced Micro Devices, Inc. Device OpenCL C version: OpenCL C 1.2 Driver version: 2639.3 Profile: FULL_PROFILE Version: OpenCL 1.2 AMD-APP (2639.3) Extensions: cl_khr_fp64 cl_amd_fp64 cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_gl_sharing cl_amd_device_attribute_query cl_amd_vec3 cl_amd_printf cl_amd_media_ops cl_amd_media_ops2 cl_amd_popcnt cl_khr_image2d_from_buffer cl_khr_spir cl_khr_gl_event

I found sometime, spmv on a non-square matrix may generate random results. Here is an example: I made a change on examples/utests.cpp: --- i/examples/utests.cpp +++ w/examples/utests.cpp

@@ -381,16 +381,22 @@ int main(int argc, char *argv[]) {
                    row.push_back(col.size());
                }

-               std::vector<double> x(m);
+               std::vector<double> x(m * 40 * 1024);
                std::vector<double> y(n);
-               std::generate(x.begin(), x.end(), []() { return (double)rand() / RAND_MAX; });
+               std::generate(x.begin(), x.end(), []() { return 0; });
+               // std::generate(x.begin(), x.end(), []() { return (double)rand() / RAND_MAX; });

                vex::SpMat <double> A(ctx.queue(), y.size(), x.size(), row.data(), col.data(), val.data());
-               vex::vector<double> X(ctx.queue(), x);
+               vex::vector<double> X(ctx.queue(), x.size());
+    X = 0;
                vex::vector<double> Y(ctx.queue(), y.size());

                Y = A * X;
                copy(Y, y);
+    for (auto i: y) {
+      std::cout << i << "\t";
+    }
+    std::cout << std::endl;

                double res = 0;
                for(size_t i = 0; i < y.size(); i++) {

===========================END OF DIFF=============================== I expect to see all the elements of y are 0. However, in my cxt:

1.    Intel(R) Core(TM) i7-3930K CPU @ 3.20GHz
   

2. GeForce GTX 680 They may be: (not always, but if they are all 0, then change 40 to something else, it's easy to see random numbers for the first run)

Sparse matrix-vector product for non-square matrix: ............0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0623769 0.952414 0.456983 0.67 0493 0.99274 0.902097 0.687297 0.163851 0.503021 0.282368 0.872369 0.101505 0.372683 0.670697 0.470279 0.42733 0.830566 0.987858 0 .908128 0.348441 0.902872 0.703435 0.485632 0.197168 0.271371 0.0163845 0.216113 0.510647 0.775435 0.0967307 0.846954 0.837812 0 .049145 0.303937 0.508305 0.0418853 0.206033 0.195602 0.205736 0.709054 0.477971 0.0781055 0.810559 0.850653 0.748802 0.280838 0 .277983 0.579368 0.268696 0.186111 0.927809 0.171569 0.889546 0.413442 0.368737 0.160917 0.429826 0.58485 0.671563 0.205261 0.68 1581 0.518517 0.0430729 0.730726 0.822454 0.551378 0.772611 0.0284869 0.74698 0.978347 0.737541 0.224951 0.0564526 0.5481 0.0756039 0 .805255 0.828938 0.353587 0.384623 0.0976346 0.539698 0.312432 0.269203 0.429244 0.725873 0.63794 0.59016 0.155699 0.22279 0.261723 0.36 096 0.904371 0.780241 0.404033 0.635096 0.602694 0.955412 0.407707 0.631181 0.702392 0.386054 0.368722 0.927343 0.442507 0 .916822 0.00294663 0.247762 0.745761 0.356533 0.632384 0.843395 0.896231 0.944816 0.112598 0.325475 0.67069 0.750539 0.915635 0.82 6389 0.973329 0.177358 0.187349 0.8777 0.957599 0.591383 0.512796 0.560293 0.546794 0.920503 0.191474 0.249186 0.306557 0.56 0196 0.176529 0.749064 0.477018 0.179476 0.996826 0.222779 0.536009 0.62921 0.0661738 0.43224 0.574026 0.178772 0.757715 0.244716 0 .929311 0.67335 0.0711045 0.90264 0.850708 0.258454 0.78034 0.808307 0.849837 0.293136 0.3686 0.396631 0.213639 0.560074 0.645818 0.520196 0 .12027 0.822347 0.26926 0.597288 0.00182245 0.266086 0.820067 0.537831 0.895296 0.886241 0.970072 0.469322 0.0650129 0.727787 0.71 4038 0.994324 0.401137 0.785142 0.896964 0.251845 0.0435961 0.677304 0.0601521 0.893433 0.970439 0.428752 0.290064 0.184078 0 .988826 0.935882 0.704274 0.109096 0.758228 0.973535 0.706384 0.760051 0.23962 0.526451 0.297882 0.134916 0.412692 0.267954 0.60 4238 0.477705 0.995741 0.318276 0.472028 0.396878 0.103418 0.368992 0.648723 0.147014 0.0462959 0.708875 0.040447 0.0167352 0 .137627 0.330511 0.200813 0.126453 0.266393 0.905088 0.235549 0.0246213 0.878622 0.941933 0.784672 0.118243 0.468384 0.0825547 0 .253159 0.881076 0.350509 0.857397 0.358781 0.34625 0.175673 0.830809 0.743128 0.279092 0.199801 0.39185 0.426106 0.246097 0.100725 0 .466553 0.262833 0.238352 0.797064 0.463646 0.364805 0.0634568 0.368734 0.600354 0.0880781 0.247356 0.542288 0.87275 0.365598 0.01 06718 0.955305 0.618757 0.891748 0.305814 0.476155 0.250528 0.652064 0.651828 0.0813374 0.395191 0.93092 0.281139 0.787042 0.35 7026 0.527236 0.887767 0.823579 0.790069 0.126119 0.620642 0.253715 0.490925 0.684099 0.622448 0.0912792 0.772177 0.869804 0 .633567 0.644928 0.235402 0.644239 0.600233 0.85416 0.535986 0.906047 0.330315 0.786515 0.55811 0.982143 0.867852 0.953302 0.913063 0 .148991 0.740343 0.270089 0.676227 0.628111 0.0936671 0.466296 0.75423 0.71431 0.72001 0.245155 0.398409 0.342458 0.336434 0.170586 0.21 2262 0.970001 0.815514 0.447664 0.614239 0.415747 0.301824 0.150226 0.321793 0.632138 0.936741 0.879904 0.614281 0.804593 0 .833205 0.527344 0.953584 0.573549 0.797433 0.62981 0.201659 0.8911 0.096106 0.955889 0.605409 0.816116 0.201044 0.00381809 0.158574 0 .537478 0.174404 0.370836 0.507479 0.989918 0.8185 0.121718 0.405665 0.120324 0.271944 0.727458 0.752462 0.208685 0.607362 0.36 6744 0.0132775 0.440568 0.894088 0.966861 0.0141164 0.69152 0.596672 0.215776 0.58262 0.692777 0.171665 0.188029 0.508894 0.372709 0 .191847 0.667468 0.910187 0.366252 0.0383039 0.417666 0.35617 0.856804 0.539385 0.761835 0.977128 0.811329 0.489294 0.729591 0.02 00135 0.0966558 0.0963346 0.0332909 0.537223 0.990422 0.000151991 0.55134 0.681943 0.596824 0.767116 0.264563 0.289601 0.938781 0.45 2592 0.798495 0.31149 0.64444 0.465962 0.221677 0.0106913 0.504266 0.639344 0.366861 0.36107 0.178728 0.128696 0.338199 0.990057 0.61 799 0.0677896 0.0100704 0.714646 0.164124 0.0433613 0.251869 0.154546 0.0435133 0.803209 0.836489 0.640337 0.570325 0.101052 0 .929938 0.509105 0.553644 0.728432 0.820595 0.198084 0.194395 0.0422728 0.208775 0.698661 0.681616 0.575636 0.0597309 0.860345 0 .704333 0.39793 0.850401 0.322323 0.465719 0.860472 0.0369686 0.629843 0.903833 0.288838 0.78439 0.947346 0.0920466 0.620879 0.587683 0 .662371 0.721931 0.517621 0.171477 0.275575 0.246053 0.992072 0.473659 0.440448 0.0343447 0.682434 0.139108 0.715961 0.25807 0.19 8839 0.576306 0.962403 0.596769 0.426707 0.284726 0.0624881 0.287179 0.321695 0.692331 0.191012 0.610532 0.476721 0.138359 0 .702579 0.0976002 0.726042 0.36495 0.819531 0.243663 0.536427 0.0951062 0.489716 0.528499 0.568765 0.930164 0.562843 0.251199 0.06 92725 0.278805 0.509269 0.268112 0.85511 0.471673 0.864881 0.281817 0.756399 0.927369 0.568996 0.0780933 0.6197 0.760008 0.688626 0 .0964217 0.898367 0.391205 0.194022 0.624409 0.756155 0.013553 0.868072 0.292581 0.108659 0.357788 0.82108 0.677424 0.287952 0 .383924 0.928623 0.357225 0.662728 0.437893 0.625337 0.517838 0.909565 0.490217 0.799656 0.665964 0.417586 0.368652 0.744057 0 .0372869 0.12866 0.432683 0.133709 0.0270274 0.823887 0.32773 0.651436 0.580042 0.341283 0.519508 0.872624 0.449943 0.877296 0.69 3704 0.127367 0.165248 0.0776274 0.0559905 0.522473 0.740356 0.493883 0.147809 0.258194 0.403449 0.638027 0.0578493 0.0694125 0 .0556134 0.426501 0.81347 0.0929003 0.555162 0.246153 0.226609 0.582189 0.0700402 0.554339 0.233625 0.650082 0.895623 0.753133 0 .522706 0.345566 0.63043 0.21641 0.472933 0.795678 0.294037 0.528923 0.318151 0.0343929 0.0228064 0.46596 0.292587 0.426255 0.103987 0.35 0436 0.495667 0.159601 0.776937 0.309137 0.252501 0.332099 0.55529 0.47911 0.914288 0.62533 0.0334494 0.147913 0.275413 0.929072 0.90 1047 0.798119 0.274638 0.531476 0.0145286 0.747571 0.327154 0.308566 0.276494 0.645305 0.342959 0.2993 0.111266 0.635546 0.72 5555 0.215253 0.985982 0.221223 0.374854 0.762919 0.53036 0.627355 0.0950179 0.0856498 0.106465 0.00930583 0.71098 0.139914 0.157219 0 .986392 0.0689863 0.058266 0.784511 0.343624 0.589743 0.79904 0.091195 0.916897 0.107606 0.367689 0.562202 0.450565 0.666989 0.673468 0.0861104 0.392544 0.88872 0.072092 0.613767 0.263574 0.835011 0.144127 0.890929 0.930029 0.229777 0.997393 0.939335 0.94 0756 0.137307 0.0965539 0.927149 0.206293 0.15482 0.71166 0.549917 0.744562 0.5107 0.641112 0.661459 0.618305 0.00880131 0.223661 0.06 88701 0.67579 0.897129 0.15498 0.0683349 0.785849 0.227073 0.682102 0.0494234 0.0620835 0.826229 failed.

Hi ddemidov and thanks for your beautiful work! Trying to compile in linux I obtain two errors:

1. the first is concerning constants.hpp: OPENCL/vexcl/vexcl/constants.hpp:178:1: error: ‘one_div_two_pi’ is not a member of ‘boost::math::constants’ I have solved this commenting the line 178 in constants.hpp.

2. the second is about tests/threads.cpp and I didn't solve it yet. /home/formica/OPENCL/vexcl/tests/threads.cpp:24:74: required from here /usr/include/boost/thread/detail/thread.hpp:114:9: error: ‘boost::thread::thread(boost::thread&)’ is private In file included from /usr/include/c++/4.7/memory:66:0, from /usr/include/boost/config/no_tr1/memory.hpp:21, from /usr/include/boost/smart_ptr/shared_ptr.hpp:27, from /usr/include/boost/shared_ptr.hpp:17, from /usr/include/boost/thread/pthread/thread_data.hpp:10, from /usr/include/boost/thread/thread.hpp:17, from /usr/include/boost/thread.hpp:13, from /home/formica/OPENCL/vexcl/tests/threads.cpp:2: /usr/include/c++/4.7/bits/stl_construct.h:77:7: error: within this context

Take care: I had to use cl.hpp version 1.1 to start the compilation with my old nvidia gpu.

Can you help me?

Dear Dennis, I have added a restricted and restricted_const keyword. however i have not discovered any increase in performance when using these.

i also added an extra cmakelist which only adds the vexcl files so they are visible if you use the cmakelist as a project file.

I edited some lines of the reduction kernel. so it doesn't copy from smem to sdata to smem again since this was a useless copy and it saved a single instruction hurray :) while not breaking the code.

Sincerely, Boris Smidt.

I am facing this error after including vexcl. Even if i specify the backend it shows the error. #include <vexcl/vexcl.hpp>

    ```

from /home/kafi/Documents/Projects/CODD-pro-lib_integration/main.cpp:14: /usr/include/boost/date_time/date_generators.hpp: In member function ‘virtual std::string boost::date_time::nth_kday_of_month<date_type>::to_string() const’: /usr/include/boost/date_time/date_generators.hpp:237:8: error: ambiguous overload for ‘operator<<’ (operand types are ‘std::basic_ostream’ and ‘int’) 236 | ss << 'M' | ~~~~~~~~~ | | | std::basic_ostream 237 | << static_cast(month_) << '.' | ^~ ~~~~~~~~~~~~~~~~~~~~~~~~ | | | int In file included from /usr/include/c++/9/iterator:64, from /usr/include/CL/cl.hpp:219,

Is it possible to Copy Memory location of an OpenCl backend using VexCL and using that location with Boost.compute? As Boost.compute uses vector type..

It looks like docs can be built only from git checkout

Running Sphinx v4.0.1                                                                                                  
                                                                                                                                                                                                                                              
Configuration error:                           
There is a programmable error in your configuration file:

Traceback (most recent call last):
  File "/usr/lib/pypy3.7/site-packages/sphinx/config.py", line 323, in eval_config_file
    exec(code, namespace)
  File "/var/tmp/portage/dev-cpp/vexcl-1.4.2/work/vexcl-1.4.2/docs/conf.py", line 78, in <module>
    version = git_version()
  File "./git_version.py", line 40, in git_version
    raise ValueError("Cannot find the version number!")
ValueError: Cannot find the version number!

I was tinkering with VexCL on an NVidia jetson nano, which only supports cuda. It appears if CL isn't available at all, the project files have issues? (Even if I install OpenCL headers, the lack of a library still causes a failure). Any hints?

-- Looking for CL/cl_platform.h -- Looking for CL/cl_platform.h - not found CMake Warning at CMakeLists.txt:180 (message): The JIT interface requires OpenCL headers to be available.You can download them from https://github.com/KhronosGroup/OpenCL-HeadersSet OpenCL_INCLUDE_DIR to the location of the headers.For now, disabling the JIT target.

CMake Error at CMakeLists.txt:244 (add_library): add_library cannot create ALIAS target "VexCL::Backend" because target "OpenCL" does not already exist.

Hi, first of all, thanks for the development and maintenance of this great library! I would like to know if there is any plan to support block sparse matrices and block SpMV with constant-size blocks. This would speedup the kernel by reducing the indexing overhead for this class of matrices.

Thank you.