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! hello world

Hello World

Setting the following environment variables is highly recommended to make life easier.

$ source <install_path>/setup.source # default install_path would be $HOME/.iris

The “Hello World” program is the first step towards learning IRIS. This program displays the message “HELLO WORLD” on the screen.

$ cd iris/apps/helloworld
$ make
$ ./helloworld
HELLO WORLD
$

Host Code

tab1

../../../apps/helloworld/helloworld.c

tab2

../../../apps/helloworld/helloworld.cpp

Kernels

tab1

../../../apps/helloworld/kernel.cu

tab2

../../../apps/helloworld/kernel.hip.cpp

tab3

../../../apps/helloworld/kernel.cl

tab4

../../../apps/helloworld/kernel.openmp.h

! saxpy

SAXPY

SAXPY stands for "Single-precision A * X Plus Y". It is a combination of scalar multiplication and vector addition.

$ cd iris/apps/saxpy
$ make
$ ./saxpy-c
X [  0.  1.  2.  3.  4.  5.  6.  7.]
Y [  0.  1.  2.  3.  4.  5.  6.  7.]
S = 10.000000 * X + Y [   0.  11.  22.  33.  44.  55.  66.  77.]
$

Host Code

tab1

../../../apps/saxpy/saxpy.c

tab2

../../../apps/saxpy/saxpy.cpp

tab3

../../../apps/saxpy/saxpy.f90

tab4

../../../apps/saxpy/saxpy.py

Kernels

tab1

../../../apps/saxpy/kernel.cu

tab2

../../../apps/saxpy/kernel.hip.cpp

tab3

../../../apps/saxpy/kernel.cl

tab4

../../../apps/saxpy/kernel.openmp.h

! data memory

Data Memory

One of the major benefits of using IRIS is its "data memory" feature, which automatically manage data movement independent of the device scheduling. Here is an example of the use of data memory during a vector addition code. Note how the:

iris_data_mem_create(&mem_A, A, SIZE * sizeof(int));
...
iris_task_dmem_flush_out(task0,mem_C);

call differs from the SAXPY example above. We no longer need iris_task_h2d_full and iris_task_d2h_full calls, instead, we only need to know when to flush the final memory transfer required by the host. This is a simpler work-flow that the conventional explicit memory movement approach.

Running

$ cd iris/apps/vecadd
$ make
$ ./vecadd-iris

Host Code

tab1

../../../apps/vecadd/vecadd-iris.cpp

Kernels

tab1

../../../apps/vecadd/kernel.cu

tab2

../../../apps/vecadd/kernel.hip.cpp

tab3

../../../apps/vecadd/kernel.cl

tab4

../../../apps/vecadd/kernel.openmp.h

! device selection

Device Selection

IRIS opportunistically attempts to use all available devices and backends, it resolves task names to function names in the corresponding kernel binaries. It allows device selection to be set both at compile and at runtime.

Compile Time

The user can submit the device target(s) for when the task is submitted:

iris_task_submit(iris_task task, int device, const char* opt, int sync);

This task submission includes information about the task, such as a hint, target device parameter, synchronization mode (blocking or non-blocking), and policy selector that indicates where the task should be executed. The device is the device submission policy. The complete list of available targets are:

Device Policy About
iris_cpu Submit the task to a CPU device
iris_gpu Submit the task to any GPU device
iris_fpga Submit the task to any FPGA (currently Intel and Xilinx)
iris_dsp Submit the task to any DSP device (currently Hexagon)
iris_nvidia Submit the task to an NVIDIA GPU device
iris_amd Submit the task to an AMD GPU device
iris_gpu_intel Submit the task to an Intel GPU device
iris_phi Submit the task to an Intel Xeon Phi device

We can also submit tasks according to a scheduling policy:

Scheduling Policy About
iris_default Use the first device
iris_roundrobin Submit this task in a round-robin (cyclic) way, for equal work sharing
iris_depend Submit this task to a device that has been assigned its dependent
iris_data Submit task to device to minimize data movement
iris_profile Submit the task to the device based on execution time history
iris_random Randomly assign this task to any of the available devices
iris_pending Delay submitting the task until the memory it depends on has been assigned, then use that device
iris_any Submit task to the device with the fewest assigned tasks
iris_all Submit the task to all device queues, the device that accesses it first has exclusive execution (it is removed from the other device queues)
iris_custom Submit the task based on a used provided, custom policy

The opt parameter is for iris_custom policies.

Runtime

We can also filter out devices at runtime by setting the IRIS_ARCHS environment variable. Modifying the selection of backends to instantiate allows dynamic device targets---without requiring recompilation. All current options are:hip,cuda,opencl, and openmp. An example of only allowing execution on openmp and hip devices would then be:

$ export IRIS_ARCHS=hip,openmp
$ ./helloworld
HELLO WORLD
$