The EPICS Software Framework Moves from Controls to Physics

The Experimental Physics and Industrial Control System (EPICS), is an open-source software framework for high-performance distributed control, and is at the heart of many of the world’s large accelerators and telescopes. Recently, EPICS has undergone a major revision, with the aim of better computing supporting for the next generation of machines and analytical tools. Many new data types, such as matrices, tables, images, and statistical descriptions, plus users’ own data types, now supplement the simple scalar and waveform types of the former EPICS. New computational architectures for scientific computing have been added for high-performance data processing services and pipelining. Python and Java bindings have enabled powerful new user interfaces. The result has been that controls are now being integrated with modelling and simulation, machine learning, enterprise databases, and experiment DAQs. We introduce this new EPICS (version 7) from the perspective of accelerator physics and review early adoption cases in accelerators around the world

Evaluating Rapid Application Development with Python for Heterogeneous Processor-based FPGAs

As modern FPGAs evolve to include more het- erogeneous processing elements, such as ARM cores, it makes sense to consider these devices as processors first and FPGA accelerators second. As such, the conventional FPGA develop- ment environment must also adapt to support more software- like programming functionality. While high-level synthesis tools can help reduce FPGA development time, there still remains a large expertise gap in order to realize highly performing implementations. At a system-level the skill set necessary to integrate multiple custom IP hardware cores, interconnects, memory interfaces, and now heterogeneous processing elements is complex. Rather than drive FPGA development from the hardware up, we consider the impact of leveraging Python to ac- celerate application development. Python offers highly optimized libraries from an incredibly large developer community, yet is limited to the performance of the hardware system. In this work we evaluate the impact of using PYNQ, a Python development environment for application development on the Xilinx Zynq devices, the performance implications, and bottlenecks associated with it. We compare our results against existing C-based and hand-coded implementations to better understand if Python can be the glue that binds together software and hardware developers.Comment: To appear in 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM'17

Software Challenges For HL-LHC Data Analysis

The high energy physics community is discussing where investment is needed to prepare software for the HL-LHC and its unprecedented challenges. The ROOT project is one of the central software players in high energy physics since decades. From its experience and expectations, the ROOT team has distilled a comprehensive set of areas that should see research and development in the context of data analysis software, for making best use of HL-LHC's physics potential. This work shows what these areas could be, why the ROOT team believes investing in them is needed, which gains are expected, and where related work is ongoing. It can serve as an indication for future research proposals and cooperations

TensorFlow Doing HPC

TensorFlow is a popular emerging open-source programming framework supporting the execution of distributed applications on heterogeneous hardware. While TensorFlow has been initially designed for developing Machine Learning (ML) applications, in fact TensorFlow aims at supporting the development of a much broader range of application kinds that are outside the ML domain and can possibly include HPC applications. However, very few experiments have been conducted to evaluate TensorFlow performance when running HPC workloads on supercomputers. This work addresses this lack by designing four traditional HPC benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG) solver and Fast Fourier Transform (FFT). We analyze their performance on two supercomputers with accelerators and evaluate the potential of TensorFlow for developing HPC applications. Our tests show that TensorFlow can fully take advantage of high performance networks and accelerators on supercomputers. Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical communication bandwidth on our testing platform. We find an approximately 2x, 1.7x and 1.8x performance improvement when increasing the number of GPUs from two to four in the matrix-matrix multiply, CG and FFT applications respectively. All our performance results demonstrate that TensorFlow has high potential of emerging also as HPC programming framework for heterogeneous supercomputers.Comment: Accepted for publication at The Ninth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES'19