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Transverse momentum (pT) spectra of pions, kaons, and protons up to pT=20 GeV/c have been measured in Pb-Pb collisions at sNN−−−√=2.76 TeV using the ALICE detector for six different centrality classes covering 0-80%. The proton-to-pion and the kaon-to-pion ratios both show a distinct peak at pT≈3 GeV/c in central Pb-Pb collisions that decreases towards more peripheral collisions. For pT>10 GeV/c, the nuclear modification factor is found to be the same for all three particle species in each centrality interval within systematic uncertainties of 10-20%. This suggests there is no direct interplay between the energy loss in the medium and the particle species composition in the hard core of the quenched jet. For pT<10 GeV/c, the data provide important constraints for models aimed at describing the transition from soft to hard physics.
Transverse momentum (pT) spectra of pions, kaons, and protons up to pT=20 GeV/c have been measured in Pb-Pb collisions at sNN−−−√=2.76 TeV using the ALICE detector for six different centrality classes covering 0-80%. The proton-to-pion and the kaon-to-pion ratios both show a distinct peak at pT≈3 GeV/c in central Pb-Pb collisions that decreases towards more peripheral collisions. For pT>10 GeV/c, the nuclear modification factor is found to be the same for all three particle species in each centrality interval within systematic uncertainties of 10-20%. This suggests there is no direct interplay between the energy loss in the medium and the particle species composition in the hard core of the quenched jet. For pT<10 GeV/c, the data provide important constraints for models aimed at describing the transition from soft to hard physics.
On development, feasibility, and limits of highly efficient CPU and GPU programs in several fields
(2013)
With processor clock speeds having stagnated, parallel computing architectures have achieved a breakthrough in recent years. Emerging many-core processors like graphics cards run hundreds of threads in parallel and vector instructions are experiencing a revival. Parallel processors with many independent but simple arithmetical logical units fail executing serial tasks efficiently. However, their sheer parallel processing power makes them predestined for parallel applications while the simple construction of their cores makes them unbeatably power efficient. Unfortunately, old programs cannot profit by simple recompilation. Adaptation often requires rethinking and modifying algorithms to make use of parallel execution. Many applications have some serial subroutines which are very hard to parallelize, hence contemporary compute clusters are often homogeneous, offering fast processors for serial tasks and parallel processors for parallel tasks. In order not to waste the available compute power, highly efficient programs are mandatory.
This thesis is about the development of fast algorithms and their implementations on modern CPUs and GPUs, about the maximum achievable efficiency with respect to peak performance and to power consumption respectively, and about feasibility and limits of programs for CPUs, GPUs, and heterogeneous systems. Three totally different applications from distinct fields, which were developed in the extent of this thesis, are presented.
The ALICE experiment at the LHC particle collider at CERN studies heavy-ion collisions at high rates of several hundred Hz, while every collision produces thousands of particles, whose trajectories must be reconstructed. For this purpose, ALICE track reconstruction and ALICE track merging have been adapted for GPUs and deployed on 64 GPU-enabled compute-nodes at CERN.
After a testing phase, the tracker ran in nonstop operation during 2012 providing full real-time track reconstruction. The tracker employs a multithreaded pipeline as well as asynchronous data transfer to ensure continuous GPU utilization and outperforms the fastest available CPUs by about a factor three.
The Linpack benchmark is the standard tool for ranking compute clusters. It solves a dense system of linear equations using primarily matrix multiplication facilitated by a routine called DGEMM. A heterogeneous GPU-enabled version of DGEMM and Linpack has been developed, which can utilize the CAL, CUDA, and OpenCL APIs as backend. Employing this implementation, the LOEWE-CSC cluster ranked place 22 in the November 2010 Top500 list of the fastest supercomputers, and the Sanam cluster achieved the second place in the November 2012 Green500 list of the most power efficient supercomputers. An elaborate lookahead algorithm, a pipeline, and asynchronous data transfer hide the serial CPU-bound tasks of Linpack behind DGEMM execution on the GPU reaching the highest efficiency on GPU-accelerated clusters.
Failure erasure codes enable failure tolerant storage of data and real-time failover, ensuring that in case of a hardware defect servers and even complete data centers remain operational. It is an absolute necessity for present-day computer infrastructure. The mathematical theory behind the codes involves matrix-computations in finite fields, which are not natively supported by modern processors and hence computationally very expensive. This thesis presents a novel scheme for fast encoding matrix generation and demonstrates a fast implementation for the encoding itself, which uses exclusively either integer or logical vector instructions. Depending on the scenario, it is always hitting different hard limits of the hardware: either the maximum attainable memory bandwidth, or the peak instruction throughput, or the PCI Express bandwidth limit when GPUs or FPGAs are used.
The thesis demonstrates that in most cases with respect to the available peak performance, GPU implementations can be as efficient as their CPU counterparts.
With respect to costs or power consumption, they are much more efficient. For this purpose, complex tasks must be split in serial as well as parallel parts and the execution must be pipelined such that the CPU bound tasks are hidden behind GPU execution. Few cases are identified where this is not possible due to PCI Express limitations or not reasonable because practical GPU languages are missing.
LICE is one of the four major LHC experiments at CERN. When the accelerator enters the Run 3 data-taking period, starting in 2021, ALICE expects almost 100 times more Pb-Pb central collisions than now, resulting in a large increase of data throughput. In order to cope with this new challenge, the collaboration had to extensively rethink the whole data processing chain, with a tighter integration between Online and Offline computing worlds. Such a system, code-named ALICE O2, is being developed in collaboration with the FAIR experiments at GSI. It is based on the ALFA framework which provides a generalized implementation of the ALICE High Level Trigger approach, designed around distributed software entities coordinating and communicating via message passing.
We will highlight our efforts to integrate ALFA within the ALICE O2 environment. We analyze the challenges arising from the different running environments for production and development, and conclude on requirements for a flexible and modular software framework. In particular we will present the ALICE O2 Data Processing Layer which deals with ALICE specific requirements in terms of Data Model. The main goal is to reduce the complexity of development of algorithms and managing a distributed system, and by that leading to a significant simplification for the large majority of the ALICE users.
The Time Projection Chamber (TPC) of the ALICE experiment at the CERN LHC was upgraded for Run 3 and Run 4. Readout chambers based on Gas Electron Multiplier (GEM) technology and a new readout scheme allow continuous data taking at the highest interaction rates expected in Pb-Pb collisions. Due to the absence of a gating grid system, a significant amount of ions created in the multiplication region is expected to enter the TPC drift volume and distort the uniform electric field that guides the electrons to the readout pads. Analytical calculations were considered to correct for space-charge distortion fluctuations but they proved to be too slow for the calibration and reconstruction workflow in Run 3. In this paper, we discuss a novel strategy developed by the ALICE Collaboration to perform distortion-fluctuation corrections with machine learning and convolutional neural network techniques. The results of preliminary studies are shown and the prospects for further development and optimization are also discussed.
ALICE (A Large Heavy Ion Experiment) is one of the four large scale experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is an online computing farm, which reconstructs events recorded by the ALICE detector in real-time. The most computing-intensive task is the reconstruction of the particle trajectories. The main tracking devices in ALICE are the Time Projection Chamber (TPC) and the Inner Tracking System (ITS). The HLT uses a fast GPU-accelerated algorithm for the TPC tracking based on the Cellular Automaton principle and the Kalman filter. ALICE employs gaseous subdetectors which are sensitive to environmental conditions such as ambient pressure and temperature and the TPC is one of these. A precise reconstruction of particle trajectories requires the calibration of these detectors. As our first topic, we present some recent optimizations to our GPU-based TPC tracking using the new GPU models we employ for the ongoing and upcoming data taking period at LHC. We also show our new approach to fast ITS standalone tracking. As our second topic, we present improvements to the HLT for facilitating online reconstruction including a new flat data model and a new data flow chain. The calibration output is fed back to the reconstruction components of the HLT via a feedback loop. We conclude with an analysis of a first online calibration test under real conditions during the Pb-Pb run in November 2015, which was based on these new features.
In LHC Run 3, ALICE will increase the data taking rate significantly to 50 kHz continuous read-out of minimum bias Pb—Pb collisions. The reconstruction strategy of the online-offline computing upgrade foresees a first synchronous online reconstruction stage during data taking enabling detector calibration and data compression, and a posterior calibrated asynchronous reconstruction stage. Many new challenges arise, among them continuous TPC read-out, more overlapping collisions, no a priori knowledge of the primary vertex and of location-dependent calibration in the synchronous phase, identification of low-momentum looping tracks, and sophisticated raw data compression. The tracking algorithm for the Time Projection Chamber (TPC) will be based on a Cellular Automaton and the Kalman filter. The reconstruction shall run online, processing 50 times more collisions per second than today, while yielding results comparable to current offline reconstruction. Our TPC track finding leverages the potential of hardware accelerators via the OpenCL and CUDA APIs in a shared source code for CPUs and GPUs for both reconstruction stages. We give an overview of the status of Run 3 tracking including performance on processors and GPUs and achieved compression ratios.