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DTSTART:19700308T020000
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DTSTAMP:20181221T160727Z
LOCATION:D175
DTSTART;TZID=America/Chicago:20181112T103000
DTEND;TZID=America/Chicago:20181112T110000
UID:submissions.supercomputing.org_SC18_sess176_ws_llvmf104@linklings.com
SUMMARY:OpenMP GPU Offload in Flang and LLVM
DESCRIPTION:Workshop\nProgram Transformation, Programming Systems, Worksho
 p Reg Pass\n\nOpenMP GPU Offload in Flang and LLVM\n\nOzen, Atzeni, Wolfe,
  Southwell, Klimowicz\n\nGraphics Processing Units (GPUs) have been widely
  adopted to accelerate the execution of High Performance Computing (HPC) w
 orkloads due to their enormous computational throughput, ability to execut
 e a large number of threads inside SIMD groups in parallel, and their use 
 of multithreaded hardware to hide long pipelining and memory access latenc
 y. However, developing applications able to exploit the high performance o
 f GPUs requires proper code tuning. As a consequence, computer scientists 
 proposed different approaches to simplify GPU programming, including direc
 tive-based programming models such as OpenMP and OpenACC. Their intention 
 is to solve the aforementioned programming challenges with a directive-bas
 ed approach which allows the users to insert non-executable pragma constru
 cts that guide the compiler to handle the low-level complexities of the sy
 stem. Flang, a Fortran front end for the LLVM Compiler Infrastructure, has
  drawn attention from the HPC community. Although Flang supports OpenMP fo
 r multicore architectures, it has no capability of offloading parallel reg
 ions to accelerator devices. In this paper, we present OpenMP Offload supp
 ort in Flang targeting NVIDIA GPUs. Our goal is to investigate possible im
 plementation strategies of OpenMP GPU offloading into Flang. The experimen
 tal results show that our approach is able to achieve performance similar 
 to existing compilers with OpenMP GPU offload support.
URL:https://sc18.supercomputing.org/presentation/?id=ws_llvmf104&sess=sess
 176
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