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[bsp2020]

Is it possible to see video of SC15 presentation? I'm interested in "Porting CUDA applications to C++ with New AMD HIP conversion tools" and "Advances in HSA Software Stack" presentation mentioned in AMD Launches ‘Boltzmann Initiative’ to Dramatically Reduce Barriers to GPU Computing on AMD FirePro™...

I used to be able to find some videos from fireuser.com. But that site seems to be gone now...

Thanks!

[gstoner · Accepted solution]

We have now released the ROCm platform ( Boltzmann Initiative)   supports the following capabilities  You can find more info now at www.gpuopen.com on the HIP runtime and compiler,  Here is little more info

• Compiler/Language Runtime
  • HCC Compiler and Language Runtime  https://gpuopen.com/compute-product/hcc-heterogeneous-compute-compiler
    • Single Source Compiler supports
    • C++ 11/14/17 Support with Parallel STL
    • OpenMP 3.1 and 4.0  C and C++ support for CPU
  • HCC is built on  CLANG and LLVM compiler foundation with Native GCN ISA code generation

• Static Offline compiler which is separate from the driver so we can be more aggressive with optimizations
• HIP Runtime https://gpuopen.com/compute-product/hip-convert-cuda%E2%80%A6-portable-c-code/ and HIPIFY which are tool we can use to port CUDA application over our hardware more easily.
• Continuum  IO Anaconda with NUMBA support for Accelerated Python
• Math Libraries
  • HcBLAS https://bitbucket.org/multicoreware/hcblas
  • HcFFT https://bitbucket.org/multicoreware/hcfft
  • HcSPARSE https://bitbucket.org/multicoreware/hcsparse
  • HcRNG  https://bitbucket.org/multicoreware/hcrng
• ROCR Runtime ( which based on the HSA runtime API  http://www.hsafoundation.com/html/HSA_Library.htm enhanced for the need for dGPU’s)  for developing tools and getting the lowest level access to the hardware you need it.
  • User Mode Queues  
  • User Mode DMA
  • Advanced Memory Management API supporting need of Multi-GPU systems
  • Coarse-grain Shared Virtual Memory
  • Peer to Peer Multi-GPU supports with Large Bar support
  • GPU enabled Peer to Peer with RDMA support for Mellanox and Chelsio Network cards
  • Process Concurrency & Preemption

• HSA Signals and Atomics (Using PCIe Gen3 Platform Atomics) for System level signaling and synchronization
• Standardized loader and Code Object Format  for Binary GCN ISA   ( ELF & DWARF support)
• The stack is Fully Open Source based leveraging  MIT and UIUC license.

To run this we need: * Haswell CPU or newer CPU, like Xeon E5 v3 * Fiji GPU R9 Fury Nano, R9 Fury X, R9 Fury * Ubuntu 14.04 or Fedora 23

You can find more information at www.gpuopen.com and at Github.com at the links

• https://github.com/RadeonOpenCompute/ROCR-Runtime/tree/dev

• https://github.com/RadeonOpenCompute/ROCK-Kernel-Driver/tree/dev

You can also find more info on GPUopen.com