Classes Of Benchmarks Research Articles

Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data.

The continued scaling of genetic perturbation technologies combined with high-dimensional assays such as cellular microscopy and RNA-sequencing has enabled genome-scale reverse-genetics experiments that go beyond single-endpoint measurements of growth or lethality. Datasets emerging from these experiments can be combined to construct perturbative "maps of biology", in which readouts from various manipulations (e.g., CRISPR-Cas9 knockout, CRISPRi knockdown, compound treatment) are placed in unified, relatable embedding spaces allowing for the generation of genome-scale sets of pairwise comparisons. These maps of biology capture known biological relationships and uncover new associations which can be used for downstream discovery tasks. Construction of these maps involves many technical choices in both experimental and computational protocols, motivating the design of benchmark procedures to evaluate map quality in a systematic, unbiased manner. Here, we (1) establish a standardized terminology for the steps involved in perturbative map building, (2) introduce key classes of benchmarks to assess the quality of such maps, (3) construct 18 maps from four genome-scale datasets employing different cell types, perturbation technologies, and data readout modalities, (4) generate benchmark metrics for the constructed maps and investigate the reasons for performance variations, and (5) demonstrate utility of these maps to discover new biology by suggesting roles for two largely uncharacterized genes.

Solving hybrid Boolean constraints in continuous space via multilinear Fourier expansions

The Boolean SATisfiability problem (SAT) is of central importance in computer science. Although SAT is known to be NP-complete, progress on the engineering side—especially that of Conflict-Driven Clause Learning (CDCL) and Local Search SAT solvers—has been remarkable. Yet, while SAT solvers, aimed at solving industrial-scale benchmarks in Conjunctive Normal Form (CNF), have become quite mature, SAT solvers that are effective on other types of constraints (e.g., cardinality constraints and XORs) are less well-studied; a general approach to handling non-CNF constraints is still lacking.To address the issue above, we design FourierSAT,1 an incomplete SAT solver based on Fourier Analysis (also known as Walsh-Fourier Transform) of Boolean functions, a technique to represent Boolean functions by multilinear polynomials. By such a reduction to continuous optimization, we propose an algebraic framework for solving systems consisting of different types of constraints. The idea is to leverage gradient information to guide the search process in the direction of local improvements. We show this reduction enjoys interesting theoretical properties. Empirical results demonstrate that FourierSAT can be a useful complement to other solvers on certain classes of benchmarks.

Concurrency Analysis in Dynamic Dataflow Graphs

Dynamic dataflow scheduling enables effective exploitation of concurrency while making parallel programming easier. To this end, analyzing the inherent degree of concurrency available in dataflow graphs is an important task, since it may aid compilers or programmers to assess the potential performance a program can achieve via parallel execution. However, traditional concurrency analysis techniques only work for DAGs (directed acyclic graphs), hence the need for new techniques that contemplate graphs with cycles. In this paper we present techniques to perform concurrency analysis on generic dynamic dataflow graphs, even in the presence of cycles. In a dataflow graph, nodes represent instructions and edges describe dependencies. The novelty of our approach is that we allow concurrency between different iterations of the loops. Consequently, a set of concurrent nodes may contain instructions from different loops that can be proven independent. In this work, we provide a set of theoretical tools for obtaining bounds and illustrate implementation of parallel dataflow runtime on a set of representative graphs for important classes of benchmarks to compare measured performance against derived bounds.

FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints

The Boolean SATisfiability problem (SAT) is of central importance in computer science. Although SAT is known to be NP-complete, progress on the engineering side—especially that of Conflict-Driven Clause Learning (CDCL) and Local Search SAT solvers—has been remarkable. Yet, while SAT solvers, aimed at solving industrial-scale benchmarks in Conjunctive Normal Form (CNF), have become quite mature, SAT solvers that are effective on other types of constraints (e.g., cardinality constraints and XORs) are less well-studied; a general approach to handling non-CNF constraints is still lacking. In addition, previous work indicated that for specific classes of benchmarks, the running time of extant SAT solvers depends heavily on properties of the formula and details of encoding, instead of the scale of the benchmarks, which adds uncertainty to expectations of running time.To address the issues above, we design FourierSAT, an incomplete SAT solver based on Fourier analysis of Boolean functions, a technique to represent Boolean functions by multilinear polynomials. By such a reduction to continuous optimization, we propose an algebraic framework for solving systems consisting of different types of constraints. The idea is to leverage gradient information to guide the search process in the direction of local improvements. Empirical results demonstrate that FourierSAT is more robust than other solvers on certain classes of benchmarks.

A vertex-similarity clustering algorithm for community detection

ABSTRACTCommunities in networks are considered to be groups of vertices with higher probability of being connected to each other than to members of other groups. Community detection, then, is a method to identify these communities based on the higher intra-cluster and lower inter-cluster connectivity. Depending on the type and size of the network, detecting such communities can be a challenging task. The method we propose is a degenerate agglomerative hierarchical clustering algorithm (DAHCA) that makes use of the reachability matrix to find a community structure in networks. We tested DAHCA using common classes of network benchmarks as well as real-world networks and compared it to state-of-the-art community detection algorithms. Our results show that it can effectively identify hierarchies of communities, and outperform some of the algorithms for more complex networks. In particular, when communities start to exhibit very low intra-community connectivity, it is the only method that is still able to identify communities.

Towards a performance-aware power capping orchestrator for the Xen hypervisor

In the last few years, multi-core processors entered into the domain of embedded systems: this, together with virtualization techniques, allows multiple applications to easily run on the same System-on-Chip (SoC). As power consumption remains one of the most impacting costs on any digital system, several approaches have been explored in literature to cope with power caps, trying to maximize the performance of the hosted applications. In this paper, we present some preliminary results and opportunities towards a performance-aware power capping orchestrator for the Xen hypervisor. The proposed solution, called XeMPUPiL, uses the Intel Running Average Power Limit (RAPL) hardware interface to set a strict limit on the processor's power consumption, while a software-level Observe-Decide-Act (ODA) loop performs an exploration of the available resource allocations to find the most power efficient one for the running workload. We show how XeMPUPiL is able to achieve higher performance under different power caps for almost all the different classes of benchmarks analyzed (e.g., CPU-, memory-and IO-bound).

Peacock: a customizable MapReduce for multicore platform

MapReduce has been demonstrated to be a promising alternative to simplify parallel programming with high performance on single multicore machine. Compared to the cluster version, MapReduce does not have bottlenecks in disk and network I/O on single multicore machine, and it is more sensitive to characteristics of workloads. A single execution flow may be inefficient for many classes of workloads. For example, the fixed execution flow of the MapReduce program structure can impose significant overheads for workloads that inherently have only one emitted value per key, which are mainly caused by the unnecessary reduce phase. In this paper, we refine the workload characterization from Phoenix++ according to the attributes of key-value pairs, and give a demonstration that the refined workload characterization model covers all classes of MapReduce workloads. Based on the model, we propose a new MapReduce system with workload-customizable execution flow. The system, namely Peacock, is implemented on top of Phoenix++. Experiments with four different classes of benchmarks on a 16-core Intel-based server show that Peacock achieves better performance than Phoenix++ for workloads that inherently have only one emitted value per key (up to a speedup of $$3.6\times $$ 3.6 × ) while identical for other classes of workloads.

A Hybrid Bat Algorithm with Path Relinking for Capacitated Vehicle Routing Problem

The capacitated vehicle routing problem (CVRP) is an NP-hard problem with wide engineering and theoretical background. In this paper, a hybrid bat algorithm with path relinking (HBA-PR) is proposed to solve CVRP. The HBA-PR is constructed based on the framework of continuous bat algorithm; the greedy randomized adaptive search procedure (GRASP) and path relinking are effectively integrated into bat algorithm. Moreover, in order to further improve the performance, the random subsequences and single-point local search are operated with certain loudness (probability). In order to verify the validity of the method in this paper, and it's efficiency and with other existing methods, several classical CVRP instances from three classes of CVRP benchmarks are selected to tested. Experimental results and comparisons show that the HBA-PR is effective for CVRP.

Fast computation of the prime implicants by exact direct-cover algorithm based on the new partial ordering operation rule

In this study, a novel OFF-set based direct-cover Exact Minimization Algorithm (EMA) is proposed for single-output Boolean functions represented in a sum-of-products form. To obtain the complete set of prime implicants covering the given Target Minterm (ON-minterm), the proposed method uses OFF-cubes (OFF-minterms) expanded by this Target Minterm. The amount of temporary results produced by this method does not exceed the size of the OFF-set. In order to achieve the goal of this study, which is to make faster computations, logic operations were used instead of the standard operations. Expansion OFF-cubes, commutative absorption operations and intersection operations are realized by logic operations for fast computation. The proposed minimization method is tested on several classes of benchmarks and then compared with the ESPRESSO algorithm. The results show that the proposed algorithm obtains more accurate and faster results than ESPRESSO does.

Permutation flowshop scheduling problems with maximal and minimal time lags

In this paper, we study permutation flowshop problems with minimal and/or maximal time lags, where the time lags are defined between couples of successive operations of jobs. Such constraints may be used to model various industrial situations, for instance the production of perishable products. We present theoretical results concerning two-machine cases, we prove that the two-machine permutation flowshop with constant maximal time lags is strongly NP-hard. We develop an optimal branch and bound procedure to solve the m -machine permutation flowshop problem with minimal and maximal time lags. We test several lower bounds and heuristics providing upper bounds on different classes of benchmarks, and we carry out a performance analysis.