PinComm: characterizing intra-application communication for the many-core era

While the number of cores in both general purpose chip-multiprocessors (CMPs) and embedded Multi-Processor Systems-on-Chip (MPSoCs) keeps rising, on-chip communication becomes more and more important. In order to write efficient programs for these architectures, it is therefore necessary to have a good idea of the communication behavior of an application. We present a communication profiler that extracts this behavior from compiled, sequential C/C++ programs, and constructs a dynamic data-flow graph at the level of major functional blocks. It can also be used to view differences between program phases, such as different video frames, which allows both input- and phase-specific optimizations to be made. Finally, PinComm can visualize inter-thread communication in parallel programs, which can help in optimizing communication behavior and spotting communication-related performance bottlenecks

The impact of global communication latency at extreme scales on Krylov methods

Krylov Subspace Methods (KSMs) are popular numerical tools for solving large linear systems of equations. We consider their role in solving sparse systems on future massively parallel distributed memory machines, by estimating future performance of their constituent operations. To this end we construct a model that is simple, but which takes topology and network acceleration into account as they are important considerations. We show that, as the number of nodes of a parallel machine increases to very large numbers, the increasing latency cost of reductions may well become a problematic bottleneck for traditional formulations of these methods. Finally, we discuss how pipelined KSMs can be used to tackle the potential problem, and appropriate pipeline depths

Sniper: scalable and accurate parallel multi-core simulation

Sniper is a next generation parallel, high-speed and accurate x86 simulator. This multi-core simulator is based on the interval core model and the Graphite simulation infrastructure, allowing for fast and accurate simulation and for trading off simulation speed for accuracy to allow a range of flexible simulation options when exploring different homogeneous and heterogeneous multi-core architectures. The Sniper simulator allows one to perform timing simulations for both multi-programmed workloads and multi-threaded, shared-memory applications running on 10s to 100+ cores, at a high speed when compared to existing simulators. The main feature of the simulator is its core model which is based on interval simulation, a fast mechanistic core model. Interval simulation raises the level of abstraction in architectural simulation which allows for faster simulator development and evaluation times; it does so by ’jumping’ between miss events, called intervals. Sniper has been validated against multi-socket Intel Core2 and Nehalem systems and provides average performance prediction errors within 25% at a simulation speed of up to several MIPS. This simulator, and the interval core model, is useful for uncore and system-level studies that require more detail than the typical one-IPC models, but for which cycle-accurate simulators are too slow to allow workloads of meaningful sizes to be simulated. As an added benefit, the interval core model allows the generation of CPI stacks, which show the number of cycles lost due to different characteristics of the system, like the cache hierarchy or branch predictor, and lead to a better understanding of each component’s effect on total system performance. This extends the use for Sniper to application characterization and hardware/software co-design. The Sniper simulator is available for download at http://snipersim.org and can be used freely for academic research

Epoch profiles: microarchitecture-based application analysis and optimization

The performance of data-intensive applications, when running on modern multi- and many-core processors, is largely determined by their memory access behavior. Its most important contributors are the frequency and latency of off-chip accesses and the extent to which long-latency memory accesses can be overlapped with useful computation or with each other. In this paper we present two methods to better understand application and microarchitectural interactions. An epoch profile is an intuitive way to understand the relationships between three important characteristics: the on-chip cache size, the size of the reorder window of an out-of-order processor, and the frequency of processor stalls caused by long-latency, off-chip requests (epochs). By relating these three quantities one can more easily understand an application’s memory reference behavior and thus significantly reduce the design space. While epoch profiles help to provide insight into the behavior of a single application, developing an understanding of a number of applications in the presence of area and core count constraints presents additional challenges. Epoch-based microarchitectural analysis is presented as a better way to understand the trade-offs for memory-bound applications in the presence of these physical constraints. Through epoch profiling and optimization, one can significantly reduce the multidimensional design space for hardware/software optimization through the use of high-level model-driven techniques

Power aware early design stage hardware software co-optimization

Co-optimizing hardware and software can lead to substantial performance and energy benefits, and is becoming an increasingly important design paradigm. In scientific computing, power constraints increasingly necessitate the return to specialized chips such as Intel’s MIC or IBM’s Blue-Gene architectures. To enable hardware/software co-design in early stages of the design cycle, we propose a simulation infrastructure methodology by combining high-abstraction performance simulation using Sniper with power modeling using McPAT and custom DRAM power models. Sniper/McPAT is fast — simulation speed is around 2 MIPS on an 8-core host machine — because it uses analytical modeling to abstract away core performance during multi-core simulation. We demonstrate Sniper/McPAT’s accuracy through validation against real hardware; we report average performance and power prediction errors of 22.1% and 8.3%, respectively, for a set of SPEComp benchmarks

Evaluating application vulnerability to soft errors in multi-level cache hierarchy

As the capacity of cache increases dramatically with new processors, soft errors originating in cache has become a major reliability concern for high performance processors. This paper presents application specific soft error vulnerability analysis in order to understand an application's responses to soft errors from different levels of caches. Based on a high-performance processor simulator called Graphite, we have implemented a fault injection framework that can selectively inject bit flips to different levels of caches. We simulated a wide range of relevant bit error patterns and measured the applications' vulnerabilities to bit errors. Our experimental results have shown the various vulnerabilities of applications to bit errors from different levels of caches; the results have also indicated the probabilities of different behaviors from the applications

BarrierPoint: sampled simulation of multi-threaded applications

Sampling is a well-known technique to speed up architectural simulation of long-running workloads while maintaining accurate performance predictions. A number of sampling techniques have recently been developed that extend well- known single-threaded techniques to allow sampled simulation of multi-threaded applications. Unfortunately, prior work is limited to non-synchronizing applications (e.g., server throughput workloads); requires the functional simulation of the entire application using a detailed cache hierarchy which limits the overall simulation speedup potential; leads to different units of work across different processor architectures which complicates performance analysis; or, requires massive machine resources to achieve reasonable simulation speedups. In this work, we propose BarrierPoint, a sampling methodology to accelerate simulation by leveraging globally synchronizing barriers in multi-threaded applications. BarrierPoint collects microarchitecture-independent code and data signatures to determine the most representative inter-barrier regions, called barrierpoints. BarrierPoint estimates total application execution time (and other performance metrics of interest) through detailed simulation of these barrierpoints only, leading to substantial simulation speedups. Barrierpoints can be simulated in parallel, use fewer simulation resources, and define fixed units of work to be used in performance comparisons across processor architectures. Our evaluation of BarrierPoint using NPB and Parsec benchmarks reports average simulation speedups of 24.7x (and up to 866.6x) with an average simulation error of 0.9% and 2.9% at most. On average, BarrierPoint reduces the number of simulation machine resources needed by 78x

Using fast and accurate simulation to explore hardware/software trade-offs in the multi-core era

Writing well-performing parallel programs is challenging in the multi-core processor era. In addition to achieving good per-thread performance, which in itself is a balancing act between instruction-level parallelism, pipeline effects and good memory performance, multi-threaded programs complicate matters even further. These programs require synchronization, and are affected by the interactions between threads through sharing of both processor resources and the cache hierarchy. At the Intel Exascience Lab, we are developing an architectural simulator called Sniper for simulating future exascale-era multi-core processors. Its goal is twofold: Sniper should assist hardware designers to make design decisions, while simultaneously providing software designers with a tool to gain insight into the behavior of their algorithms and allow for optimization. By taking architectural features into account, our simulator can provide more insight into parallel programs than what can be obtained from existing performance analysis tools. This unique combination of hardware simulator and software performance analysis tool makes Sniper a useful tool for a simultaneous exploration of the hardware and software design space for future high-performance multi-core systems

RecoNoC: a reconfigurable network-on-chip

This article presents the design of RecoNoC: a compact, highly flexible FPGA-based network-on-chip (NoC), that can be easily adapted for various experiments. In this work, we enhanced this NoC with dynamically reconfigurable shortcuts. These can be used to alter the NoC's topology to adapt to the system's communication needs. The design has been implemented and tested on a Xilinx Virtex-2 Pro FPGA, using the TMAP dynamic datafolding toolflow to automatically generate the reconfigurable hardware and the software reconfiguration procedures. The results show that, using dynamic datafolding, the overhead of introducing this shortcut mechanism is limited

Cycle-accurate evaluation of reconfigurable photonic networks-on-chip

There is little doubt that the most important limiting factors of the performance of next-generation Chip Multiprocessors (CMPs) will be the power efficiency and the available communication speed between cores. Photonic Networks-on-Chip (NoCs) have been suggested as a viable route to relieve the off- and on-chip interconnection bottleneck. Low-loss integrated optical waveguides can transport very high-speed data signals over longer distances as compared to on-chip electrical signaling. In addition, with the development of silicon microrings, photonic switches can be integrated to route signals in a data-transparent way. Although several photonic NoC proposals exist, their use is often limited to the communication of large data messages due to a relatively long set-up time of the photonic channels. In this work, we evaluate a reconfigurable photonic NoC in which the topology is adapted automatically (on a microsecond scale) to the evolving traffic situation by use of silicon microrings. To evaluate this system's performance, the proposed architecture has been implemented in a detailed full-system cycle-accurate simulator which is capable of generating realistic workloads and traffic patterns. In addition, a model was developed to estimate the power consumption of the full interconnection network which was compared with other photonic and electrical NoC solutions. We find that our proposed network architecture significantly lowers the average memory access latency (35% reduction) while only generating a modest increase in power consumption (20%), compared to a conventional concentrated mesh electrical signaling approach. When comparing our solution to high-speed circuit-switched photonic NoCs, long photonic channel set-up times can be tolerated which makes our approach directly applicable to current shared-memory CMPs