Μεταφορά με Χαμηλή Κατανάλωση Ενέργειας ενός Βιολογικά Ακριβούς Μοντέλου Κυττάρων Κάτω Ελαίας στο Single-Chip Cloud Computer

45 σ.Το Single-Chip Cloud Computer (SCC) είναι "μια πειραματική πλατφόρμα με 48 πυρήνες Πέντιουμ από την Intel Labs". Με σκοπό την εξερεύνηση των δυνατοτήτων του SCC, δύο εφαρμογές μεταφέρθηκαν στην πλατφόρμα στα πλαίσια της διπλωματικής. Η πρώτη εφαρμογή είναι ένας συμπιεστής εικόνων βάση του πρωτοκόλλου της ομάδας "Joint Photographics Experts Group" (JPEG). Η μεταφορά του συμπιεστή στο SCC πετυχαίνει αύξηση της ταχύτητας επί της μονονηματικής έκδοσης της εφαρμογής και προσφέρει έναν αποδεκτό τελικό ρυθμό παραγωγής εικόνων για τις σημερινές ανάγκες της βιομηχανίας. Η δεύτερη και κύρια εφαρμογή που μεταφέρθηκε στο SCC είναι ένας προσομοιωτής δικτύων των εγκεφαλικών κυττάρων πυρήνα κάτω ελαίας, με στόχο την ελάττωση κατανάλωσης ενέργειας λειτουργίας. Καταπιάνεται με την εξερεύνηση του ανθρώπινου εγκεφάλου που απασχολεί μεγάλο μέρος της ακαδημαϊκής έρευνας σήμερα. Διαφοροποιείται από τις συνηθισμένες προσεγγίσεις τύπου "μαύρου κούτιου" καθώς βασίζεται σε ένα βιολογικά ακριβές μοντέλο. Η μεταφορά στο SCC επικεντρώνεται στην εύρεση μεθόδων για τη βελτιστοποίηση της απόδοσης του προσομοιωτή σε συνδυασμό με την μείωση του ενεργειακού κόστους της εφαρμογής. Επί τούτου αναπτύχθηκαν δύο μέθοδοι μεταφοράς στο SCC, καθεμία με διαφορετικό τρόπο μείωσης της ενεργειακής κατανάλωσης. Οι δύο μέθοδοι συγκρίνονται βάση γραφημάτων και παρουσιάζεται μια λύση που ισορροπεί ανάμεσα στην ταχύτητα και το καλό ενεργειακό προφίλ της εφαρμογής.The Single-Chip Cloud Computer(SCC) is an experimental board with 48 Pentium cores created by Intel Labs. To explore SCC, this thesis covers the porting of two applications on the board. The first project is the porting of an encoder for the "Joint Photographics Experts Group (JPEG) for still image compression". Porting the encoder on the SCC yields a satisfactory speedup of the single-threaded version and achieves an acceptable frame rate with respect to today's standards of the industry. The second and main application ported on the SCC is an energy-aware simulator of inferior olive cell networks. It tackles an important aspect of the human brain's exploration, a subject motivating academic research greatly in modern times. It differs from the usual black-box approach on the matter by using a biologically accurate model. The porting focuses on finding efficient solutions to optimizing the simulator's performance while reducing energy expenditure and power consumption. To this end, two different porting options are introduced, each with a different method of lowering power requirements. The different methods are compared against each other with extensive Figures detailing each option's results. Ultimately, a solution that balances performance and energy gain is presented.Γιώργος Θ. Χατζηκωνσταντή

From Knights Corner to Landing: a Case Study Based on a Hodgkin-Huxley Neuron Simulator

Brain modeling has been presenting significant challenges to the world of high-performance computing (HPC) over the years. The field of computational neuroscience has been developing a demand for physiologically plausible neuron models, that feature increased complexity and thus, require greater computational power. We explore Intel’s newest generation of Xeon Phi computing platforms, named Knights Landing (KNL), as a way to match the need for processing power and as an upgrade over the previous generation of Xeon Phi models, the Knights Corner (KNC). Our neuron simulator of choice features a Hodgkin-Huxley-based (HH) model which has been ported on both generations of Xeon Phi platforms and aggressively draws on both platforms’ computational assets. The application uses the OpenMP interface for efficient parallelization and the Xeon Phi’s vectorization buffers for Single-Instruction Multiple Data (SIMD) processing. In this study we offer insight into the efficiency with which the application utilizes the assets of the two Xeon Phi generations and we evaluate the merits of utilizing the KNL over its predecessor. In our case, an out-of-the-box transition on Knights Landing, offers on average 2.4× speed up while consuming 48 % less energy than KNC

GPU Implementation of Neural-Network Simulations Based on Adaptive-Exponential Models

Detailed brain modeling has been presenting significant challenges to the world of high-performance computing (HPC), posing computational problems that can benefit from modern hardware-acceleration technologies. We explore the capacity of GPUs for simulating large-scale neuronal networks based on the Adaptive Exponential neuron-model, which is widely used in the neuroscientific community. Our GPU-powered simulator acts as a benchmark to evaluate the strengths and limitations of modern GPUs, as well as to explore their scaling properties when simulating large neural networks. This work presents an optimized GPU implementation that outperforms a reference multicore implementation by 50x, whereas utilizing a dual-GPU configuration can deliver a speedup of 90x for networks of 20,000 fully interconnected AdEx neurons

First impressions from detailed brain model simulations on a xeon/xeon-phi node

The development of physiologically plausible neuron models comes with increased complexity, which poses a challenge for many-core computing. In this work, we have chosen an extension of the demanding Hodgkin-Huxley model for the neurons of the Inferior Olivary Nucleus, an area of vital im-portance for motor skills. The computing fabric of choice is an Intel Xeon-Xeon Phi system, widely-used in modern computing infrastructure. The target application is paral-lelized with combinations of MPI and OpenMP. The best configurations are scaled up to human InfOli numbers

From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator

Brain modeling has been presenting significant challenges to the world of high-performance computing (HPC) over the years. The field of computational neuroscience has been developing a demand for physiologically plausible neuron models, that feature increased complexity and thus, require greater computational power. We explore Intel’s newest generation of Xeon Phi computing platforms, named Knights Landing (KNL), as a way to match the need for processing power and as an upgrade over the previous generation of Xeon Phi models, the Knights Corner (KNC). Our neuron simulator of choice features a Hodgkin-Huxley-based (HH) model which has been ported on both generations of Xeon Phi platforms and aggressively draws on both platforms’ computational assets. The application uses the OpenMP interface for efficient parallelization and the Xeon Phi’s vectorization buffers for Single-Instruction Multiple Data (SIMD) processing. In this study we offer insight into the efficiency with which the application utilizes the assets of the two Xeon Phi generations and we evaluate the merits of utilizing the KNL over its predecessor. In our case, an out-of-the-box transition on Knights Landing, offers on average 2.4x speed up while consuming 48% less energy than KNC