oneapi-src / oneapi-src/oneAPI-samples

GPU acceleration support is not available

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bug
Langage dominant
C++
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Description

Summary

There is an issue where graphics acceleration support for 13th generation Intel CPUs is not available.

Is there a way to verify if GPU acceleration is possible?

Why is my laptop unable to recognize it?

Version

I tested it using the image from https://hub.docker.com/r/intel/oneapi.

Environment

lscpu - click to extand 
Architecture:             x86_64
  CPU op-mode(s):         32-bit, 64-bit
  Address sizes:          39 bits physical, 48 bits virtual
  Byte Order:             Little Endian
CPU(s):                   16
  On-line CPU(s) list:    0-15
Vendor ID:                GenuineIntel
  Model name:             13th Gen Intel(R) Core(TM) i7-1360P
    CPU family:           6
    Model:                186
    Thread(s) per core:   2
    Core(s) per socket:   12
    Socket(s):            1
    Stepping:             2
    CPU(s) scaling MHz:   31%
    CPU max MHz:          5000.0000
    CPU min MHz:          400.0000
    BogoMIPS:             5222.40
    Flags:                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc ar
                          t arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 
                          xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhance
                          d tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetb
                          v1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes 
                          vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
Virtualization features:  
  Virtualization:         VT-x
Caches (sum of all):      
  L1d:                    448 KiB (12 instances)
  L1i:                    640 KiB (12 instances)
  L2:                     9 MiB (6 instances)
  L3:                     18 MiB (1 instance)
NUMA:                     
  NUMA node(s):           1
  NUMA node0 CPU(s):      0-15
Vulnerabilities:          
  Gather data sampling:   Not affected
  Itlb multihit:          Not affected
  L1tf:                   Not affected
  Mds:                    Not affected
  Meltdown:               Not affected
  Mmio stale data:        Not affected
  Reg file data sampling: Mitigation; Clear Register File
  Retbleed:               Not affected
  Spec rstack overflow:   Not affected
  Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
  Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
  Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
  Srbds:                  Not affected
  Tsx async abort:        Not affected
lspci -vvnn | grep "VGA compatible controller" - click to expand 
00:02.0 VGA compatible controller [0300]: Intel Corporation Raptor Lake-P [Iris Xe Graphics] [8086:a7a0] (rev 04) (prog-if 00 [VGA controller])

I ran the Docker image with the -it option and proceeded as follows:

cd /
mkdir workspace
cd workspace
git clone https://github.com/oneapi-src/oneAPI-samples.git

Steps to reproduce

I conducted a total of three tests.

1. For the test at https://github.com/oneapi-src/oneAPI-samples/tree/master/Libraries/oneDNN/getting_started:

mkdir build
cd build
cmake ..
make
export DNNL_VERBOSE=1
./bin/getting-started-cpp
Result
onednn_verbose,info,oneDNN v3.5.0 (commit 302c601036103dd8391ac583030abd2e19a75f92)
onednn_verbose,info,cpu,runtime:DPC++,nthr:16
onednn_verbose,info,cpu,isa:Intel AVX2 with Intel DL Boost
onednn_verbose,info,gpu,runtime:DPC++
onednn_verbose,info,cpu,engine,0,backend:OpenCL,name:13th Gen Intel(R) Core(TM) i7-1360P,driver_version:2024.18.6,binary_kernels:disabled
onednn_verbose,info,graph,backend,0:dnnl_backend
onednn_verbose,primitive,info,template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time
onednn_verbose,graph,info,template:operation,engine,partition_id,partition_kind,op_names,data_formats,logical_tensors,fpmath_mode,backend,exec_time
onednn_verbose,primitive,exec,cpu,eltwise,jit:avx2,forward_inference,data_f32::blocked:acdb::f0 diff_undef::undef:::,,alg:eltwise_relu alpha:0 beta:0,1x3x13x13,0.231201
Example passed on CPU.

if I run:

./bin/getting-started-cpp gpu
Result
root@e77315b5cda4:/workspace/oneAPI-samples/Libraries/oneDNN/getting_started/build# ./bin/getting-started-cpp gpu
Application couldn't find GPU, please run with CPU instead.

2. For the test at https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/C++SYCL/DenseLinearAlgebra/vector-add:

mkdir build
cd build
cmake ..
make spu-gpu
./vector-add-buffers
Result
root@e77315b5cda4:/workspace/oneAPI-samples/DirectProgramming/C++SYCL/DenseLinearAlgebra/vector-add/build# ./vector-add-buffers
Running on device: 13th Gen Intel(R) Core(TM) i7-1360P
Vector size: 10000
[0]: 0 + 0 = 0
[1]: 1 + 1 = 2
[2]: 2 + 2 = 4
...
[9999]: 9999 + 9999 = 19998
Vector add successfully completed on device.
  1. For the test at https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Horovod_Distributed_Deep_Learning:

Even when I run !sycl-ls, only the CPU is listed.

Observed behavior

Cannot find my GPU

Expected behavior

GPU acceleration works

Guide de contribution

Ouvrir le guide de contribution

Par où commencer

  1. Lisez l'issue en entier, puis le guide de contribution du projet.
  2. Signalez en commentaire que vous la prenez — cela évite que deux personnes fassent le même travail.
  3. Forkez le dépôt et travaillez sur une branche.
  4. Ouvrez une pull request qui référence le numéro de l'issue.

Piste de recherche

Commencez par les chemins d’exemple oneDNN getting_started, C++SYCL DenseLinearAlgebra/vector-add et IntelTensorFlow_Horovod_Distributed_Deep_Learning mentionnés dans le rapport. Reproduisez la configuration Docker, exécutez sycl-ls et comparez les résultats des commandes CPU et GPU. La tâche est terminée lorsque vous avez identifié pourquoi le Intel GPU signalé n’est pas disponible et documenté ou corrigé le comportement de l’exemple concerné.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Évaluation

Stack technique
cpp, docker
Domaine
devtools, machine-learning
Type d'issue
Bug
Difficulté
4/5
Temps estimé
3-5 jours
Activité
À l'abandon
Clarté
À clarifier
Accessibilité débutants
25/100

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