Heterogeneous Architecture and Computing

V.P. Sampath is a senior member of IEEE and a member of Institution of Engineers India. He is currently working as technical architect at AdeptChips, Bengaluru. He is a regular contributor to national newspapers, IEEE-MAS section, and has published international papers on VLSI and networks

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FAST-CUDA Block Diagram
FASTCUDA Block Diagram

The FASTCUDA toolset (Fig. 8) is responsible for automating most of this process, thus minimising user intervention.

FASTCUDA toolset
Fig. 8: FASTCUDA toolset

Design space exploration

The first step is to decide how to make the best use of the available FPGA resources for a given CUDA program. The next is to know what percentage of the FPGA real estate should be allocated to the multi-core processor for software kernels, and what percentage should be allocated to the accelerator for hardware kernels. This is followed by the information on which kernels are to be run in software and which in hardware, area-speed tradeoff best for each of the harware kernels and optimal configuration (number of cores, cache sizes, memory banks, etc) for the multi-core processor. This is done by carefully examining through several simulation and synthesis runs.

The simulation tool provides runtime estimates for execution of each kernel in software, for several configurations of the multi-core processor (with varying cache sizes, memory banks, etc). The synthesis tool provides latency estimates for execution of each kernel in hardware, with varying hardware footprints (trading area for speed).The design space exploration tool uses these area and performance estimates, along with its full knowledge of the underlying platform’s resources and available configurations, to heuristically search for the best answers to the questions listed above. User experience can be used to guide the tool, e.g., by restricting the search to a smaller set with the most ‘interesting’ multi-core configurations.

Multi-core processor

The CUDA host program as well as software kernels, i.e., the subset of kernels determined by the design space exploration tool, run in software on the multi-core processor (Fig. 9).
It uses Xilinx Microblaze soft cores with separate instruction caches and a shared data cache, all communicating through two AXI4-based buses. FASTCUDA follows a similar mapping of the threads with a GPU. Each core executes thread-block, which can use the core’s scratchpad memory as a private local memory. All the threads from any thread block can access the global shared memory, which can also be accessed by the hardware accelerator. The AXI4_Lite bus is used for communication between the multi-core processor and the accelerator block that is running hardware kernels. A simple handshake protocol is employed to pass the arguments and trigger a specific hardware kernel to start running, which will then respond back when it has finished running. Lastly, the timer and mutex blocks on the AXI4_Litebus are a requirement for symmetric multiprocessing support of the runtime on the processor.

 Multi-core processor
Fig. 9: Multi-core processor

Implementing CUDA kernels on the multi-processor

The OS-level software running on the multi-core processor here is a modified version of the Xilinx kernel ‘Xilkernel.’ Xilkernel supports POSIX threads, mutexes and semaphores, but was meant to run on a single core, thus having no support for an SMP environment like the one here. We consequently had to add SMP support to Xilkernel. CUDA kernels are supposed to run on SIMT devices (GPUs), which are drastically different from multi-core processors. Thus, the next step is to port the CUDA kernels, using MCUDA, to run on top of the multi-core multi-threaded environment provided by modified Xilkernel.

MCUDA transforms the CUDA code into a thread-based C code that uses MCUDA library in order to create a pool of threads and coordinate thread operations as well as provide the basic CUDA runtime functionality for kernel invocation and data movements. Xilkernel provides the mutex support required by MCUDA library and the thread-based support required by multi-threaded software kernels. In CUDA the host program is usually run on a chip separate from the CUDA kernels; the first is run on a general-purpose CPU and the latter on a GPU. Thus the CUDA programming model assumes that the host and device maintain their own separate memory spaces, referred to as host memory and device memory, respectively.

CUDA kernel porting process using MCUDA
Fig. 10. CUDA kernel porting process using MCUDA

The execution of a kernel involves:

1. Memory transfers of input vectors from the host memory to the device memory,
2. Kernel execution, which uses input vectors in order to generate output vectors and
3. Memory transfers of output vectors from the device memory to the host memory.

Addresses of input and output vectors are passed as arguments to the CUDA kernel. In contrast, FASTCUDA runs everything on the same chip, thus favouring a different memory model where all the threads of a kernel and the host program can share a single global memory. In this model, hardware kernels inside the accelerator have direct access to the memory in order to read their input vectors and write output vectors.

Implementing CUDA kernels in hardware

In FASTCUDA, the code of hardware kernels is preprocessed before synthesis. To aid in this preprocessing the programmer is required to use ‘#pragma’ directives in order to specify which ones among the kernel arguments are inputs and outputs, as well as their sizes. The result of translation from CUDA to SystemC is given below:

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