GPU SDK Correspondence and Device Targeting Table

GPU Targets Table

When using OpenCL SDKs, targeting CPU and GPU at the same time is sometimes possible. This is especially true for Intel OpenCL SDK and AMD APP SDK.

You can find below a table of correspondence:

SDK	CPU Intel/AMD	GPU Intel	GPU AMD	GPU NVIDIA
Intel SDK for OpenCL	Supported	Supported *	Supported	Untested
AMD APP SDK ***	Supported	Untested *	Supported	Fails
NVIDIA CUDA Toolkit	Fails **	Fails **	Fails **	Supported

Legend:

* Not usable directly.
** Reported as unsupported in public forums.
*** AMD has decided to drop the support for APP SDK and deleted all links to installation packages. You can download the installation package for Linux from our GitHub repo.

AMD GPUs using Intel SDK for OpenCL is not a typo, nor AMD APP SDK compatibility with CPUs.

Targeting Table

We present the following scenarii:

CPU, no GPU
Single CPU and GPU (even with integrated graphics)
Multiple CPU/GPU

We provide test R code below, but you can use the language of your choice with the examples of your choices:

library(lightgbm)
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
train$data[, 1] <- 1:6513
dtrain <- lgb.Dataset(train$data, label = train$label)
data(agaricus.test, package = "lightgbm")
test <- agaricus.test
dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
valids <- list(test = dtest)

params <- list(objective = "regression",
               metric = "rmse",
               device = "gpu",
               gpu_platform_id = 0,
               gpu_device_id = 0,
               nthread = 1,
               boost_from_average = FALSE,
               num_tree_per_iteration = 10,
               max_bin = 32)
model <- lgb.train(params,
                   dtrain,
                   2,
                   valids,
                   min_data = 1,
                   learning_rate = 1,
                   early_stopping_rounds = 10)

Using a bad gpu_device_id is not critical, as it will fallback to:

gpu_device_id = 0 if using gpu_platform_id = 0
gpu_device_id = 1 if using gpu_platform_id = 1

However, using a bad combination of gpu_platform_id and gpu_device_id will lead to a crash (you will lose your entire session content). Beware of it.

Your system might have multiple GPUs from different vendors ("platforms") installed. You can use the clinfo utility to identify the GPUs on each platform. On Ubuntu, you can install clinfo by executing sudo apt-get install clinfo. On Windows, you can find a list of your OpenCL devices using the utility GPUCapsViewer. If you have a discrete GPU by AMD/NVIDIA and an integrated GPU by Intel, make sure to select the correct gpu_platform_id to use the discrete GPU.

CPU Only Architectures

When you have a single device (one CPU), OpenCL usage is straightforward: gpu_platform_id = 0, gpu_device_id = 0

This will use the CPU with OpenCL, even though it says it says GPU.

Example:

> params <- list(objective = "regression",
+                metric = "rmse",
+                device = "gpu",
+                gpu_platform_id = 0,
+                gpu_device_id = 0,
+                nthread = 1,
+                boost_from_average = FALSE,
+                num_tree_per_iteration = 10,
+                max_bin = 32)
> model <- lgb.train(params,
+                    dtrain,
+                    2,
+                    valids,
+                    min_data = 1,
+                    learning_rate = 1,
+                    early_stopping_rounds = 10)
[LightGBM] [Info] This is the GPU trainer!!
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data: 6513, number of used features: 116
[LightGBM] [Info] Using requested OpenCL platform 0 device 1
[LightGBM] [Info] Using GPU Device: Intel(R) Core(TM) i7-4600U CPU @ 2.10GHz, Vendor: GenuineIntel
[LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...
[LightGBM] [Info] GPU programs have been built
[LightGBM] [Info] Size of histogram bin entry: 12
[LightGBM] [Info] 40 dense feature groups (0.12 MB) transfered to GPU in 0.004540 secs. 76 sparse feature groups.
[LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=16 and max_depth=8
[1]:    test's rmse:1.10643e-17
[LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=5
[2]:    test's rmse:0

Single CPU and GPU (even with integrated graphics)

If you have integrated graphics card (Intel HD Graphics) and a dedicated graphics card (AMD, NVIDIA), the dedicated graphics card will automatically override the integrated graphics card. The workaround is to disable your dedicated graphics card to be able to use your integrated graphics card.

When you have multiple devices (one CPU and one GPU), the order is usually the following:

GPU: gpu_platform_id = 0, gpu_device_id = 0, sometimes it is usable using gpu_platform_id = 1, gpu_device_id = 1 but at your own risk!
CPU: gpu_platform_id = 0, gpu_device_id = 1

Example of GPU (gpu_platform_id = 0, gpu_device_id = 0):

> params <- list(objective = "regression",
+                metric = "rmse",
+                device = "gpu",
+                gpu_platform_id = 0,
+                gpu_device_id = 0,
+                nthread = 1,
+                boost_from_average = FALSE,
+                num_tree_per_iteration = 10,
+                max_bin = 32)
> model <- lgb.train(params,
+                    dtrain,
+                    2,
+                    valids,
+                    min_data = 1,
+                    learning_rate = 1,
+                    early_stopping_rounds = 10)
[LightGBM] [Info] This is the GPU trainer!!
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data: 6513, number of used features: 116
[LightGBM] [Info] Using GPU Device: Oland, Vendor: Advanced Micro Devices, Inc.
[LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...
[LightGBM] [Info] GPU programs have been built
[LightGBM] [Info] Size of histogram bin entry: 12
[LightGBM] [Info] 40 dense feature groups (0.12 MB) transfered to GPU in 0.004211 secs. 76 sparse feature groups.
[LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=16 and max_depth=8
[1]:    test's rmse:1.10643e-17
[LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=5
[2]:    test's rmse:0

Example of CPU (gpu_platform_id = 0, gpu_device_id = 1):

> params <- list(objective = "regression",
+                metric = "rmse",
+                device = "gpu",
+                gpu_platform_id = 0,
+                gpu_device_id = 1,
+                nthread = 1,
+                boost_from_average = FALSE,
+                num_tree_per_iteration = 10,
+                max_bin = 32)
> model <- lgb.train(params,
+                    dtrain,
+                    2,
+                    valids,
+                    min_data = 1,
+                    learning_rate = 1,
+                    early_stopping_rounds = 10)
[LightGBM] [Info] This is the GPU trainer!!
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data: 6513, number of used features: 116
[LightGBM] [Info] Using requested OpenCL platform 0 device 1
[LightGBM] [Info] Using GPU Device: Intel(R) Core(TM) i7-4600U CPU @ 2.10GHz, Vendor: GenuineIntel
[LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...
[LightGBM] [Info] GPU programs have been built
[LightGBM] [Info] Size of histogram bin entry: 12
[LightGBM] [Info] 40 dense feature groups (0.12 MB) transfered to GPU in 0.004540 secs. 76 sparse feature groups.
[LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=16 and max_depth=8
[1]:    test's rmse:1.10643e-17
[LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=5
[2]:    test's rmse:0

When using a wrong gpu_device_id, it will automatically fallback to gpu_device_id = 0:

> params <- list(objective = "regression",
+                metric = "rmse",
+                device = "gpu",
+                gpu_platform_id = 0,
+                gpu_device_id = 9999,
+                nthread = 1,
+                boost_from_average = FALSE,
+                num_tree_per_iteration = 10,
+                max_bin = 32)
> model <- lgb.train(params,
+                    dtrain,
+                    2,
+                    valids,
+                    min_data = 1,
+                    learning_rate = 1,
+                    early_stopping_rounds = 10)
[LightGBM] [Info] This is the GPU trainer!!
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data: 6513, number of used features: 116
[LightGBM] [Info] Using GPU Device: Oland, Vendor: Advanced Micro Devices, Inc.
[LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...
[LightGBM] [Info] GPU programs have been built
[LightGBM] [Info] Size of histogram bin entry: 12
[LightGBM] [Info] 40 dense feature groups (0.12 MB) transfered to GPU in 0.004211 secs. 76 sparse feature groups.
[LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=16 and max_depth=8
[1]:    test's rmse:1.10643e-17
[LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=7 and max_depth=5
[2]:    test's rmse:0

Do not ever run under the following scenario as it is known to crash even if it says it is using the CPU because it is NOT the case:

One CPU and one GPU
gpu_platform_id = 1, gpu_device_id = 0

> params <- list(objective = "regression",
+                metric = "rmse",
+                device = "gpu",
+                gpu_platform_id = 1,
+                gpu_device_id = 0,
+                nthread = 1,
+                boost_from_average = FALSE,
+                num_tree_per_iteration = 10,
+                max_bin = 32)
> model <- lgb.train(params,
+                    dtrain,
+                    2,
+                    valids,
+                    min_data = 1,
+                    learning_rate = 1,
+                    early_stopping_rounds = 10)
[LightGBM] [Info] This is the GPU trainer!!
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data: 6513, number of used features: 116
[LightGBM] [Info] Using requested OpenCL platform 1 device 0
[LightGBM] [Info] Using GPU Device: Intel(R) Core(TM) i7-4600U CPU @ 2.10GHz, Vendor: Intel(R) Corporation
[LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...
terminate called after throwing an instance of 'boost::exception_detail::clone_impl<boost::exception_detail::error_info_injector<boost::compute::opencl_error> >'
  what():  Invalid Program

This application has requested the Runtime to terminate it in an unusual way.
Please contact the application's support team for more information.

Multiple CPU and GPU

If you have multiple devices (multiple CPUs and multiple GPUs), you will have to test different gpu_device_id and different gpu_platform_id values to find out the values which suits the CPU/GPU you want to use. Keep in mind that using the integrated graphics card is not directly possible without disabling every dedicated graphics card.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

GPU-Targets.rst

GPU-Targets.rst

GPU SDK Correspondence and Device Targeting Table

GPU Targets Table

Targeting Table

CPU Only Architectures

Single CPU and GPU (even with integrated graphics)

Multiple CPU and GPU

Files

GPU-Targets.rst

Latest commit

History

GPU-Targets.rst

File metadata and controls

GPU SDK Correspondence and Device Targeting Table

GPU Targets Table

Targeting Table

CPU Only Architectures

Single CPU and GPU (even with integrated graphics)

Multiple CPU and GPU