Above are papers related to the following paper: [Paper_ID] Paper #1129835. 	[Title] Multipath Execution: Opportunities and Limits [Abstract] Even sophisticated branch-prediction techniques necessarily suffer some mispredictions, and even relatively small mispredict rates hurt performance substantially in current-generation processors. In this paper, we investigate schemes for improving performance in the face of imperfect branch predictors by having the processor simultaneously execute code from both the taken and not-taken outcomes of a branch. This paper presents data regarding the limits of multipath execution, considers fetch-bandwidth needs for multipath execution, and discusses various dynamic confidence-prediction schemes that gauge the likelihood of branch mispredictions. Our evaluations consider executing along several (28) paths at once. Using 4 paths and a relatively simple confidence predictor, multipath execution garners speedups of up to 30% compared to the single-path case, with an average speedup of 14.4% for the SPECint suite. While associated increases in instruction-fetch-bandwidth requirements are not too surprising, a less expected result is the significance of having a separate return-address stack for each forked path. Overall, our results indicate that multipath execution offers significant improvements over single-path performance, and could be especially useful when combined with multithreading so that hardware costs can be amortized over both approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #193347. 	[Title] Selective Eager Execution on the PolyPath Architecture [Abstract] Control-flow misprediction penalties are a major impediment to high performance in wide-issue superscalar processors. In this paper we present Selective Eager Execution (SEE), an execution model to overcome mis-speculation penalties by executing both paths after diffident branches. We present the micro-architecture of the PolyPath processor, which is an extension of an aggressive superscalar, out-of-order architecture. The PolyPath architecture uses a novel instruction tagging and register renaming mechanism to execute instructions from multiple paths simultaneously in the same processor pipeline, while retaining maximum resource availability for single-path code sequences. Performance results of our detailed execution-driven, pipeline-level simulations show that the SEE concept achieves a potential average performance improvement of 48% on the SPECint95 benchmarks. A realistic implementation with a dynamic branch confidence estimator can improve performance by as much as 36% for the go benchmark, and an average of 14% on SPECint95, when compared to a normal superscalar, out-of-order, speculative execution, monopath processor. Moreover, our architectural model is both elegant and practical to implement, using a small amount of additional state and control logic. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25413. 	[Title] Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading [Abstract] A version of this paper will appear in ACM Transactions on Computer Systems, August 1997. Permission to make digital copies of part or all of this work for personal or classroom use is grantedwithout fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Abstract To achieve high performance, contemporary computer systems rely on two forms of parallelism: instruction-level parallelism (ILP) and thread-level parallelism (TLP). Wide-issue superscalar processors exploit ILP by executing multiple instructions from a single program in a single cycle. Multiprocessors (MP) exploit TLP by executing different threads in parallel on different processors. Unfortunately, both parallel-processing styles statically partition processor resources, thus preventing them from adapting to dynamically-changing levels of ILP and TLP in a program. With insufficient TLP, processors in an MP will be idle; with insufficient ILP, multiple-issue hardware on a superscalar is wasted. This paper explores parallel processing on an alternative architecture, simultaneous multithreading (SMT), which allows multiple threads to compete for and share all of the processors resources every cycle. The most compelling reason for running parallel applications on an SMT processor is its ability to use thread-level parallelism and instruction-level parallelism interchangeably. By permitting multiple threads to share the processors functional units simultaneously, the processor can use both ILP and TLP to accommodate variations in parallelism. When a program has only a single thread, all of the SMT processors resources can be dedicated to that thread; when more TLP exists, this parallelism can compensate for a lack of Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131719. 	[Title] Dynamic Hammock Predication for Non-predicated Instruction Set Architectures [Abstract] Conventional speculative architectures use branch prediction to evaluate the most likely execution path during program execution. However, certain branches are difficult to predict. One solution to this problem is to evaluate both paths following such a conditional branch. Predicated execution can be used to implement this form of multi-path execution. Predicated architectures fetch and issue instructions that have associated predicates. These predicates indicate if the instruction should commit its result. Predicating a branch reduces the number of branches executed, eliminating the chance of branch misprediction at the cost of executing additional instructions. In this paper, we propose a restricted form of multi-path execution called Dynamic Predication for architectures with little or no support for predicated instructions in their instruction set. Dynamic predication dynamically predicates instruction sequences in the form of a branch hammock, concurrently executing both paths of the branch. A branch hammock is a short forward branch that spans a few instructions in the form of an if-then or if-then-else construct. We mark these and other constructs in the executable. When the decode stage detects such a sequence, it passes a predicated instruction sequence to a dynamically scheduled execution core. Our results show that dynamic predication can accrue speedups of up to 13%. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #193352. 	[Title] Limited Dual Path Execution [Abstract] This work presents a hybrid branch predictor scheme that uses a limited form of dual path execution along with dynamic branch prediction to improve execution times. The ability to execute down both paths of a conditional branch enables the branch penalty to be minimized; however, relying exclusively on dual path execution is infeasible due because instruction fetch rates far exceed the capability of the pipeline to retire a single branch before others must be processed. By using confidence information, available in the dynamic branch prediction state tables, a limited form of dual path execution becomes feasible. This reduces the burden on the branch predictor by allowing predictions of low confidence to be avoided. In this study we present a new approach to gather branch prediction confidence with little or no overhead, and use this confidence mechanism to determine whether dual path execution or branch prediction should be used. Comparing this hybrid predictor model to the dynamic branch predictor shows a dramatic decrease in misprediction rate, which translates to an reduction in runtime of over 20%. These results imply that dual path execution, which often is thought to be an excessively resource consuming method, may be a worthy approach if restricted with an appropriate predicting set. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #193354. 	[Title] Threaded Multiple Path Execution [Abstract] This paper presents Threaded Multi-Path Execution (TME), which exploits existing hardware on a Simultaneous Multi-threading (SMT) processor to speculatively execute multiple paths of execution. When there are fewer threads in an SMT processor than hardware contexts, threaded multi-path execution uses spare contexts to fetch and execute code along the less likely path of hard-to-predict branches. This paper describes the hardware mechanisms needed to enable an SMT processor to efficiently spawn speculative threads for threaded multi-path execution. The Mapping Synchronization Bus is described, which enables the spawning of these multiple paths. Policies are examined for deciding which branches to fork, and for managing competition between primary and alternate path threads for critical resources. Our results show that TME increases the single program performance of an SMT with eight thread contexts by 14%-23% on average, depending on the misprediction penalty, for programs with a high misprediction rate. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #11148. 	[Title] Exploiting Choice: Instruction Fetch and Issue on an Implementable Simultaneous Multithreading Processor [Abstract] Simultaneous multithreading is a technique that permits multiple independent threads to issue multiple instructions each cycle. In previous work we demonstrated the performance potential of simultaneous multithreading, based on a somewhat idealized model. In this paper we show that the throughput gains from simultaneous multithreading can be achieved without extensive changes to a conventional wide-issue superscalar, either in hardware structures or sizes. We present an architecture for simultaneous multithreading that achieves three goals: (1) it minimizes the architectural impact on the conventional superscalar design, (2) it has minimal performance impact on a single thread executing alone, and (3) it achieves significant throughput gains when running multiple threads. Our simultaneous multithreading architecture achieves a throughput of 5.4 instructions per cycle, a 2.5-fold improvement over an unmodified superscalar with similar hardware resources. This speedup is enhanced by an advantage of multithreading previously unexploited in other architectures: the ability to favor for fetch and issue those threads most efficiently using the processor each cycle, thereby providing the best instructions to the processor. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #348305. 	[Title] d d The Effects of Predicated Execution on Branch Prediction [Abstract] This paper analyzes a variety of existing predication models for eliminating branch operations, and the effect that this elimination has on the branch prediction schemes in existing processors, including single issue architectures with simple prediction mechanisms, to the newer multi-issue designs with correspondingly more sophisticated branch predictors. The effect on branch prediction accuracy, branch penalty and basic block size is studied. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #612306. 	[Title] Confidence Estimation for Speculation Control [Abstract] Modern processors improve instruction level parallelism by speculation. The outcome of data and control decisions is predicted, and the operations are speculatively executed and only committed if the original predictions were correct. There are a number of other ways that processor resources could be used, such as threading or eager execution. As the use of speculation increases, we believe more processors will need some form of speculation control to balance the benefits of speculation against other possible activities. Confidence estimation is one technique that can be exploited by architects for speculation control. In this paper, we introduce performance metrics to compare confidence estimation mechanisms, and argue that these metrics are appropriate for speculation control. We compare a number of confidence estimation mechanisms, focusing on mechanisms that have a small implementation cost and gain benefit by exploiting characteristics of branch predictors, such as clustering of mispredicted branches. We compare the performance of the different confidence estimation methods using detailed pipeline simulations. Using these simulations, we show how to improve some confidence estimators, providing better insight for future investigations comparing and applying confidence estimators. Please summarise all the given information.

Paper #1129835 has the title `Multipath Execution: Opportunities and Limits  
` and the abstract `Even sophisticated branch-prediction techniques necessarily suffer some mispredictions, and even relatively small mispredict rates hurt performance substantially in current-generation processors. In this paper, we investigate schemes for improving performance in the face of imperfect branch predictors by having the processor simultaneously execute code from both the taken and not-taken outcomes of a branch. This paper presents data regarding the limits of multipath execution, considers fetch-bandwidth needs for multipath execution, and discusses various dynamic confidence-prediction schemes that gauge the likelihood of branch mispredictions. Our evaluations consider executing along several (28) paths at once. Using 4 paths and a relatively simple confidence predictor, multipath execution garners speedups of up to 30% compared to the single-path case, with an average speedup of 14.4% for the SPECint suite. While associated increases in instruction-fetch-bandwidth requirements are not too surprising, a less expected result is the significance of having a separate return-address stack for each forked path. Overall, our results indicate that multipath execution offers significant improvements over single-path performance, and could be especially useful when combined with multithreading so that hardware costs can be amortized over both approaches. 
`.
Question: Which category should Paper #1129835 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1129835 has the title `Multipath Execution: Opportunities and Limits  
` and the abstract `Even sophisticated branch-prediction techniques necessarily suffer some mispredictions, and even relatively small mispredict rates hurt performance substantially in current-generation processors. In this paper, we investigate schemes for improving performance in the face of imperfect branch predictors by having the processor simultaneously execute code from both the taken and not-taken outcomes of a branch. This paper presents data regarding the limits of multipath execution, considers fetch-bandwidth needs for multipath execution, and discusses various dynamic confidence-prediction schemes that gauge the likelihood of branch mispredictions. Our evaluations consider executing along several (28) paths at once. Using 4 paths and a relatively simple confidence predictor, multipath execution garners speedups of up to 30% compared to the single-path case, with an average speedup of 14.4% for the SPECint suite. While associated increases in instruction-fetch-bandwidth requirements are not too surprising, a less expected result is the significance of having a separate return-address stack for each forked path. Overall, our results indicate that multipath execution offers significant improvements over single-path performance, and could be especially useful when combined with multithreading so that hardware costs can be amortized over both approaches. 
`.
Question: Which category should Paper #1129835 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Rule_Learning