Table driven prediction for recursive descent LL(k) parsers

Programming languages are typically described in BNF or some extension of BNF, and the process of converting these descriptions into parsers is performed by parser generators. Some of the parser generators that convert LL grammars into parsers construct them to use recursive descent that gives them context during execution. The context is provided by the execution stack as the parser descends into the grammar and this is what allows the full expressiveness of LL grammars. Table driven parsers can be generated instead but restrictions are placed on the LL grammars that can be accepted. The benefit of tables is that they facilitate a separation of syntax analysis and semantic code written by a language designer. They are also faster and they simplify language implementation and modification. This paper proposes the possibility of a hybrid system that makes decisions using tables but once decisions are made recursive descent is employed to maintain context. The benefits of each system are maintained, and the drawbacks are mitigated. Also discussed are the modifications made to an existing parser generator, oops (version 2), so that it accepts LL(k) grammars and builds parsers using this system as proof-of-concept

The Cilkview scalability analyzer

The Cilkview scalability analyzer is a software tool for profiling, estimating scalability, and benchmarking multithreaded Cilk++ ap-plications. Cilkview monitors logical parallelism during an instru-mented execution of the Cilk++ application on a single process-ing core. As Cilkview executes, it analyzes logical dependencies within the computation to determine its work and span (critical-path length). These metrics allow Cilkview to estimate parallelism and predict how the application will scale with the number of pro-cessing cores. In addition, Cilkview analyzes scheduling overhead using the concept of a “burdened dag, ” which allows it to diagnose performance problems in the application due to an insufficient grain size of parallel subcomputations. Cilkview employs the Pin dynamic-instrumentation framework to collect metrics during a serial execution of the application code. It operates directly on the optimized code rather than on a debug version. Metadata embedded by the Cilk++ compiler in the binary executable identifies the parallel control constructs in the executing application. This approach introduces little or no overhead to the program binary in normal runs. Cilkview can perform real-time scalability benchmarking auto-matically, producing gnuplot-compatible output that allows devel-opers to compare an application’s performance with the tool’s pre-dictions. If the program performs beneath the range of expectation, the programmer can be confident in seeking a cause such as insuf-ficient memory bandwidth, false sharing, or contention, rather than inadequate parallelism or insufficient grain size

ThreadScan: Automatic and Scalable Memory Reclamation

The concurrent memory reclamation problem is that of devising a way for a deallocating thread to verify that no other concurrent threads hold references to a memory block being deallocated. To date, in the absence of automatic garbage collection, there is no satisfactory solution to this problem. Existing tracking methods like hazard pointers, reference counters, or epoch-based techniques like RCU, are either prohibitively expensive or require significant programming expertise, to the extent that implementing them efficiently can be worthy of a publication. None of the existing techniques are automatic or even semi-automated. In this paper, we take a new approach to concurrent memory reclamation: instead of manually tracking access to memory locations as done in techniques like hazard pointers, or restricting shared accesses to specific epoch boundaries as in RCU, our algorithm, called ThreadScan, leverages operating system signaling to automatically detect which memory locations are being accessed by concurrent threads. Initial empirical evidence shows that ThreadScan scales surprisingly well and requires negligible programming effort beyond the standard use of Malloc and Free

The AMT: What’s Wrong and How to Fix It

The alternative minimum tax (AMT) is a complex, unfair, and inefficient shadow tax system that threatens to affect 32 million taxpayers by 2010, many of them solidly middle class. Under current law, repealing the AMT without offsets would cost more than $850 billion through 2017. This paper summarizes the current and projected effects of the AMT and considers options to finance repeal. One attractive option we consider would be to combine AMT repeal with a four percent tax on AGI in excess of$200,000 for married couples and $100,000 for others

The AMT: What’s Wrong and How to Fix It

The alternative minimum tax (AMT) is a complex, unfair, and inefficient shadow tax system that threatens to affect 32 million taxpayers by 2010, many of them solidly middle class. Under current law, repealing the AMT without offsets would cost more than $850 billion through 2017. This paper summarizes the current and projected effects of the AMT and considers options to finance repeal. One attractive option we consider would be to combine AMT repeal with a four percent tax on AGI in excess of$200,000 for married couples and $100,000 for others

Inducing and exploiting activation sparsity for fast neural network inference

Optimizing convolutional neural networks for fast inference has recently become an extremely active area of research. One of the go-to solutions in this context is weight pruning, which aims to reduce computational and memory footprint by removing large subsets of the connections in a neural network. Surprisingly, much less attention has been given to exploiting sparsity in the activation maps, which tend to be naturally sparse in many settings thanks to the structure of rectified linear (ReLU) activation functions. In this paper, we present an in-depth analysis of methods for maximizing the sparsity of the activations in a trained neural network, and show that, when coupled with an efficient sparse-input convolution algorithm, we can leverage this sparsity for significant performance gains. To induce highly sparse activation maps without accuracy loss, we introduce a new regularization technique, coupled with a new threshold-based sparsification method based on a parameterized activation function called Forced-Activation-Threshold Rectified Linear Unit (FATReLU). We examine the impact of our methods on popular image classification models, showing that most architectures can adapt to significantly sparser activation maps without any accuracy loss. Our second contribution is showing that these these compression gains can be translated into inference speedups: we provide a new algorithm to enable fast convolution operations over networks with sparse activations, and show that it can enable significant speedups for end-to-end inference on a range of popular models on the large-scale ImageNet image classification task on modern Intel CPUs, with little or no retraining cost

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

Comprehensive molecular characterization of gastric adenocarcinoma

Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies