[docs] docker runner examples updated
@@ -23,8 +23,8 @@ We also maintain a live TornadoVM whitepaper document which you can download [he
_We have tested our docker images for CentOS >= 7.4 and Ubuntu >= 16.04._
We currently have docker images for NVIDIA and Intel Integrated GPUs using OpenJDK 8 and GraalVM for JDK 8 and 11:
-* TornadoVM docker image for NVIDIA GPUs
-* TornadoVM docker image for Intel Integrated Graphics
+* TornadoVM docker images for NVIDIA GPUs
+* TornadoVM docker images for Intel Integrated Graphics
##### TornadoVM Docker for NVIDIA GPUs:
@@ -56,6 +56,17 @@ In our [docker-tornado](https://github.com/beehive-lab/docker-tornado) repositor
./run_nvidia_graalvm-jdk11.sh tornado ...
```
+Example:
+
+```bash
+## Run with TornadoVM on an NVIDIA GPU using OpenJDK 8
+$ ./run_nvidia.sh tornado example/MatrixMultiplication 2048 ## Running on NVIDIA GP100
+Computing MxM of 2048x2048
+ CPU Execution: 0.36 GFlops, Total time = 48254 ms
+ GPU Execution: 277.09 GFlops, Total Time = 62 ms
+ Speedup: 778x
+```
+
##### TornadoVM Docker for Intel Integrated GPUs:
@@ -78,21 +89,9 @@ $ docker pull beehivelab/tornado-intel-igpu-graalvm-jdk11:latest
$ run_intel_graalvm_jdk11.sh tornado ...
```
-
See our [docker-tornado](https://github.com/beehive-lab/docker-tornado) repository for more details.
-##### Example:
-
-```bash
-## Run with TornadoVM on an NVIDIA GPU using OpenJDK 8
-$ ./run_nvidia.sh tornado example/MatrixMultiplication 2048 ## Running on NVIDIA GP100
-Computing MxM of 2048x2048
- CPU Execution: 0.36 GFlops, Total time = 48254 ms
- GPU Execution: 277.09 GFlops, Total Time = 62 ms
- Speedup: 778x
-```
-
## What can I do with TornadoVM?
We have a use-case, [kfusion-tornadovm](https://github.com/beehive-lab/kfusion-tornadovm), for accelerating a computer-vision application implemented in Java using the Tornado-API to run on GPUs.
