Section: New Results
Participants : Pierre Michaud, Bharath Narasimha Swamy, Sylvain Collange, Erven Rohou, André Seznec, Arthur Perais, Surya Khizakanchery Natarajan, Sajith Kalathingal, Tao Sun, Andrea Mondelli, Aswinkumar Sridharan, Alain Ketterlin.
Processor, cache, locality, memory hierarchy, branch prediction, multicore, power, temperature
Multicore processors have now become mainstream for both general-purpose and embedded computing. Instead of working on improving the architecture of the next generation multicore, with the DAL project, we deliberately anticipate the next few generations of multicores. While multicores featuring 1000s of cores might become feasible around 2020, there are strong indications that sequential programming style will continue to be dominant. Even future mainstream parallel applications will exhibit large sequential sections. Amdahl's law indicates that high performance on these sequential sections is needed to enable overall high performance on the whole application. On many (most) applications, the effective performance of future computer systems using a 1000-core processor chip will significantly depend on their performance on both sequential code sections and single threads.
We envision that, around 2020, the processor chips will feature a few complex cores and many (maybe 1000's) simpler, more silicon and power effective cores.
In the DAL research project, http://www.irisa.fr/alf/dal , we explore the microarchitecture techniques that will be needed to enable high performance on such heterogeneous processor chips. Very high performance will be required on both sequential sections, -legacy sequential codes, sequential sections of parallel applications-, and critical threads on parallel applications, -e.g. the main thread controlling the application. Our research focuses essentially on enhancing single process performance.
Participants : André Seznec, Pierre Michaud.
We submitted 3 predictors to the 4th Championship Branch Prediction that took place along with the ISCA 2014 conference  ,  ,  . Our predictors combine some branch prediction techniques that we introduced in our previous works, in particular TAGE  and GEHL  . The predictor we submitted to the 4KB and 32KB tracks was ranked first  in both tracks. The 3 predictors we submitted to the unlimited-size track took the first three ranks. We have established a new reference point for branch predictability limits  .
The 12 competing predictors were mostly using already published branch prediction techniques. The main learning from this year's contest is that choosing the right combination of techniques for the given constraints is at least as important as trying to specialize branch predictors for certain branch behaviors.
Revisiting Value Prediction
Participants : Arthur Perais, André Seznec.
Value prediction was proposed in the mid 90's to enhance the performance of high-end microprocessors. The research on Value Prediction techniques almost vanished in the early 2000's as it was more effective to increase the number of cores than to dedicate some silicon area to Value Prediction. However high end processor chips currently feature 8-16 high-end cores and the technology will allow to implement 50-100 of such cores on a single die in a foreseeable future. Amdahl's law suggests that the performance of most workloads will not scale to that level. Therefore, dedicating more silicon area to value prediction in high-end cores might be considered as worthwhile for future multicores.
First, we introduce a new value predictor VTAGE harnessing the global branch history  . VTAGE directly inherits the structure of the indirect jump predictor ITTAGE  . VTAGE is able to predict with a very high accuracy many values that were not correctly predicted by previously proposed predictors, such as the FCM predictor and the stride predictor. Three sources of information can be harnessed by these predictors: the global branch history, the differences of successive values and the local history of values. Moreover, VTAGE does not suffer from short critical prediction loops and can seamlessly handle back-to-back predictions, contrarily to previously proposed, hard to implement FCM predictors.
Second, we show that all predictors are amenable to very high accuracy at the cost of some loss on prediction coverage  . This greatly diminishes the number of value mispredictions and allows to delay validation until commit-time. As such, no complexity is added in the out-of-order engine because of VP (save for ports on the register file) and pipeline squashing at commit-time can be used to recover. This is crucial as adding selective replay in the OoO core would tremendously increase complexity.
Third, we leverage the possibility of validating predictions at commit to introduce a new microarchitecture, EOLE  . EOLE features Early Execution to execute simple instructions whose operands are ready in parallel with Rename and Late Execution to execute simple predicted instructions and high confidence branches just before Commit. EOLE depends on Value Prediction to provide operands for Early Execution and predicted instructions for Late Execution. However, Value Prediction requires EOLE to become truly practical. That is, EOLE allows to reduce the out-of-order issue-width by 33% without impeding performance. As such, the number of ports on the register file diminishes. Furthermore, optimizations of the register file such as banking further reduce the number of required ports. Overall EOLE possesses a register file whose complexity is on-par with that of a regular wider-issue superscalar while the out-of-order components (scheduler, bypass) are greatly simplified. Moreover, thanks to Value Prediction, speedup is obtained on many benchmarks of the SPEC'00/'06 suite.
Skewed Compressed Caches
Participant : André Seznec.
Cache compression seeks the benefits of a larger cache with the area and power of a smaller cache. Ideally, a compressed cache increases effective capacity by tightly compacting compressed blocks, has low tag and metadata overheads, and allows fast lookups. Previous compressed cache designs, however, fail to achieve all these goals. In this study, we propose the Skewed Compressed Cache (SCC), a new hardware compressed cache that lowers overheads and increases performance. SCC tracks super- blocks to reduce tag overhead, compacts blocks into a variable number of sub-blocks to reduce internal fragmentation, but retains a direct tag-data mapping to find blocks quickly and eliminate extra metadata (i.e., no backward pointers). SCC does this using novel sparse super-block tags and a skewed associative mapping that takes compressed size into account. In our experiments, SCC provides on average 8% (up to 22%) higher performance, and on average 6% (up to 20%) lower total energy, achieving the benefits of the recent Decoupled Compressed Cache  with a factor of 4 lower area overhead and lower design complexity.
This study was done in collaboration with Somayeh Sardashti and David Wood from University of Wisconsin.
Efficient Execution on Guarded Instruction Sets
Participant : André Seznec.
ARM ISA based processors are no longer low complexity processors. Nowadays, ARM ISA based processor manufacturers are struggling to implement medium-end to high-end processor cores which implies implementing a state-of-the-art out-of-order execution engine. Unfortunately providing efficient out-of-order execution on legacy ARM codes may be quite challenging due to guarded instructions.
Predicting the guarded instructions addresses the main serialization impact associated with guarded instructions execution and the multiple definition problem. Moreover, guard prediction allows to use a global branch-and-guard history predictor to predict both branches and guards, often improving branch prediction accuracy. Unfortunately such a global branch-and-guard history predictor requires the systematic use of guard predictions. In that case, poor guard prediction accuracy would lead to poor overall performance on some applications.
Building on top of recent advances in branch prediction and confidence estimation, we propose a hybrid branch and guard predictor, combining a global branch history component and global branch-and-guard history component. The potential gain or loss due to the systematic use of guard prediction is dynamically evaluated at run-time. Two computing modes are enabled: systematic guard prediction use and high confidence only guard prediction use. Our experiments show that on most applications, an overwhelming majority of guarded instructions are predicted. Therefore a relatively inefficient but simple hardware solution can be used to execute the few unpredicted guarded instructions. Significant performance benefits are observed on most applications while applications with poorly predictable guards do not suffer from performance loss  .
This study is accepted to ACM Transactions on Architecture and Compiler Optimizations (to appear January 2015) and will be presented at the HIPEAC conference in January 2015.
Participants : Andrea Mondelli, Pierre Michaud, André Seznec.
In the last 10 years, the clock frequency of high-end superscalar processors did not increase significantly. Performance keeps being increased mainly by integrating more cores on the same chip and by introducing new instruction set extensions. However, this benefits only to some applications and requires rewriting and/or recompiling these applications. A more general way to increase performance is to increase the IPC, the number of instructions executed per cycle.
We argue that some of the benefits of technology scaling should be used to increase the IPC of future superscalar cores. Starting from microarchitecture parameters similar to recent commercial high-end cores, we show that an effective way to increase the IPC is to increase the issue width. But this must be done without impacting the clock cycle. We propose to combine two known techniques: clustering and register write specialization. The objective of past work on clustered microarchitecture was to allow a higher clock frequency while minimizing the IPC loss. This led researchers to consider narrow-issue clusters. Our objective, instead, is to increase the IPC without impacting the clock cycle, which means wide-issue clusters. We show that, on a wide-issue dual cluster, a very simple steering policy that sends 64 consecutive instructions to the same cluster, the next 64 instructions to the other cluster, and so on, permits tolerating an inter-cluster delay of several cycles. We also propose a method for decreasing the energy cost of sending results of one cluster to the other cluster.
This work is currently under submission.
Adaptive Intelligent Memory Systems
Participants : André Seznec, Aswinkumar Sridharan.
On multicores, the processors are sharing the memory hierarchy, buses, caches, and memory. The performance of any single application is impacted by its environment and the behavior of the other applications co-running on the multicore. Different strategies have been proposed to isolate the behavior of the different co-running applications, for example performance isolation cache partitioning, while several studies have addressed the global issue of optimizing throughput through the cache management.
However these studies are limited to a few cores (2-4-8) and generally feature mechanisms that cannot scale to 50-100 cores. Moreover so far the academic propositions have generally taken into account a single parameter, the cache replacement policy or the cache partitioning. Other parameters such as cache prefetching and its aggressiveness already impact the behavior of a single thread application on a uniprocessor. Cache prefetching policy of each thread will also impact the behavior of all the co-running threads.
Our objective is to define an Adaptive and Intelligent Memory System management hardware, AIMS. The goal of AIMS will be to dynamically adapt the different parameters of the memory hierarchy access for each individual co-running process in order to achieve a global objective such as optimized throughput, thread fairness or respecting quality of services for some privileged threads.
Microarchitecture Performance Modeling
Multiprogram throughput of multicore/SMT processors
Participant : Pierre Michaud.
This research was done in collaboration with Stijn Eyerman and Wouter Rogiest from Ghent University.
There are several aspects to the performance of a multicore processor. One of them is multiprogram throughput, that is, how fast a multicore can execute several independent jobs. However, defining throughput metrics that are both meaningful and practical for computer architecture studies is not straightforward. We present a method to construct throughput metrics in a systematic way: we start by expressing assumptions on job sizes, job types distribution, scheduling, etc., that together define a theoretical throughput experiment. The throughput metric is then the average throughput of this experiment. Different assumptions lead to different metrics, so one should be aware of these assumptions when making conclusions based on results using a specific metric. Throughput metrics should always be defined from explicit assumptions, because this leads to a better understanding of the implications and limits of the results obtained with that metric. We elaborate multiple metrics based on different assumptions. In particular, we show that commonly used throughput metrics such as instructions per cycle and weighted speedup implicitly assume a variable workload, that is, a workload which depends on the machine being evaluated. However, in many situations, it is more realistic to assume a fixed workload. Hence we propose some new fixed-workload throughput metrics. Evaluating these new metrics requires to solve a continuous-time Markov chain. We released a software, TPCalc, that takes as input the performance results of individual coschedules simulations and computes fixed-workload throughput, taking advantage of multicore symmetries  .
In a subsequent work, we applied our framework to symbiotic scheduling on a symmetric multicore or SMT processor. Symbiotic scheduling tries to exploit the fact, because of resource sharing (execution units, caches, memory bandwidth, etc.) and because different jobs have different characteristics, the performance may be increased by carefully choosing the coschedules. We show that, when assuming a fixed workload, an optimal schedule maximizing throughput can be found by solving a linear programming problem. However, the throughput gains we observed in our experiments, 3% on average, are significantly smaller than what we expected based on published studies on symbiotic scheduling. We analyzed the reasons for this and we found the two main reasons for this discrepancy: previous studies either did not consider a fixed workload but a variable one, or did not report throughput gains but response time reductions. Response time reductions can be artificially magnified by setting the job arrival rate close to the maximum throughput.
This work will be presented at the ISPASS 2015 conference.
Modeling multi-threaded programs execution time in the many-core era
Participants : Surya Khizakanchery Natarajan, Bharath Narasimha Swamy, André Seznec.
Estimating the potential performance of parallel applications on the yet-to-be-designed future many cores is very speculative. The traditional laws used to predict performance of an application do not reflect on the various scaling behaviour of a multi-threaded (MT) application leading to optimistic estimation of performance in manycore era. In this paper, we study the scaling behavior of MT applications as a function of input workload size and the number of cores. For some MT applications in the benchmark suites we analysed, our study shows that the serial fraction in the program increases with input workload size and can be a scalability-limiting factor. Similar to previous studies  , we find that using a powerful core (heterogeneous architecture) to execute this serial part of the program can mitigate the impact of serial scaling and improve the overall performance of an application in many-core era  .
Participants : Bharath Narasimha Swamy, Alain Ketterlin, André Seznec.
Heterogeneous Many Cores (HMC) architectures that mix many simple/small cores with a few complex/large cores are emerging as a design alternative that can provide both fast sequential performance for single threaded workloads and power-efficient execution for throughput oriented parallel workloads. The availability of many small cores in a HMC presents an opportunity to utilize them as low-power helper cores to accelerate memory-intensive sequential programs mapped to a large core. However, the latency overhead of accessing small cores in a loosely coupled system limits their utility as helper cores. Also, it is not clear if small cores can execute helper threads sufficiently in advance to benefit applications running on a larger, much powerful, core. In  , we present a hardware/software framework called core-tethering to support efficient helper threading on heterogeneous many-cores. Core-tethering provides a co-processor like interface to the small cores that (a) enables a large core to directly initiate and control helper execution on the helper core and (b) allows efficient transfer of execution context between the cores, thereby reducing the performance overhead of accessing small cores for helper execution. Our evaluation on a set of memory intensive programs chosen from the standard benchmark suites show that, helper threads using moderately sized small cores can significantly accelerate a larger core compared to using a hardware prefetcher alone. We find that a small core provides a good trade-off against using an equivalent large core to run helper threads in a HMC. Additionally, helper prefetching on small cores when used along with hardware prefetching, can provide an alternate design point to growing instruction window size for achieving higher sequential performance on memory intensive applications.
Branch Prediction and Performance of Interpreter
Participants : Erven Rohou, André Seznec, Bharath Narasimha Swamy.
Interpreters have been used in many contexts. They provide portability and ease of development at the expense of performance. The literature of the past decade covers analysis of why interpreters are slow, and many software techniques to improve them. A large proportion of these works focuses on the dispatch loop, and in particular on the implementation of the switch statement: typically an indirect branch instruction. Folklore attributes a significant penalty to this branch, due to its high misprediction rate. We revisit this assumption, considering state-of-the-art branch predictors and the three most recent Intel processor generations on current interpreters. Using both hardware counters on Haswell, the latest Intel processor generation, and simulation of the ITTAGE, we show that the accuracy of indirect branch prediction is no longer critical for interpreters. We further compare the characteristics of these interpreters and analyze why the indirect branch is less important than before.
This study  has been accepted for publication at CGO 2015 (International Symposium on Code Generation and Optimization).
Augmenting superscalar architecture for efficient many-thread parallel execution
Participants : Sylvain Collange, André Seznec, Sajith Kalathingal.
We aim at exploring the design of a unique core that efficiently runs both sequential and massively parallel sections. We explore how the architecture of a complex superscalar core has to be modified or enhanced to efficiently run several threads from the same application.
Rather than vectorize at compile-time, our approach is to dynamically vectorize SPMD programs at the micro-architectural level. The SMT-SIMD hybrid core we propose extracts data parallelism from thread parallelism by scheduling groups of threads in lockstep, in a way inspired by the execution model of GPUs. As in GPUs, conditional branches whose outcomes differ between threads are handled with conditionally masked execution. However, while GPUs rely on explicit re-convergence instructions to restore lockstep execution, we target existing general-purpose instruction sets, in order to run legacy binary programs. Thus, the main challenge consists in detecting re-convergence points dynamically.
To handle this difficulty, we can build on  . In this work done in collaboration with Fernando Pereira and his team at UFMG, Brasil, we proposed instruction fetch policies that apply heuristics to maximize the cycles spent in lockstep execution, and evaluated them under a micro-architecture independent model  . Results highlight the necessity of a tradeoff between maximizing throughput and extracting data-level parallelism with lockstep execution.