Representing numeric data in 32 bits while preserving 64-bit precision

Data files often consist of numbers having only a few significant decimal digits, whose information content would allow storage in only 32 bits. However, we may require that arithmetic operations involving these numbers be done with 64-bit floating-point precision, which precludes simply representing the data as 32-bit floating-point values. Decimal floating point gives a compact and exact representation, but requires conversion with a slow division operation before it can be used. Here, I show that interesting subsets of 64-bit floating-point values can be compactly and exactly represented by the 32 bits consisting of the sign, exponent, and high-order part of the mantissa, with the lower-order 32 bits of the mantissa filled in by table lookup, indexed by bits from the part of the mantissa retained, and possibly from the exponent. For example, decimal data with 4 or fewer digits to the left of the decimal point and 2 or fewer digits to the right of the decimal point can be represented in this way using the lower-order 5 bits of the retained part of the mantissa as the index. Data consisting of 6 decimal digits with the decimal point in any of the 7 positions before or after one of the digits can also be represented this way, and decoded using 19 bits from the mantissa and exponent as the index. Encoding with such a scheme is a simple copy of half the 64-bit value, followed if necessary by verification that the value can be represented, by checking that it decodes correctly. Decoding requires only extraction of index bits and a table lookup. Lookup in a small table will usually reference cache; even with larger tables, decoding is still faster than conversion from decimal floating point with a division operation. I discuss how such schemes perform on recent computer systems, and how they might be used to automatically compress large arrays in interpretive languages such as R

Program representation size in an intermediate language with intersection and union types

The CIL compiler for core Standard ML compiles whole programs using a novel typed intermediate language (TIL) with intersection and union types and flow labels on both terms and types. The CIL term representation duplicates portions of the program where intersection types are introduced and union types are eliminated. This duplication makes it easier to represent type information and to introduce customized data representations. However, duplication incurs compile-time space costs that are potentially much greater than are incurred in TILs employing type-level abstraction or quantification. In this paper, we present empirical data on the compile-time space costs of using CIL as an intermediate language. The data shows that these costs can be made tractable by using sufficiently fine-grained flow analyses together with standard hash-consing techniques. The data also suggests that non-duplicating formulations of intersection (and union) types would not achieve significantly better space complexity.National Science Foundation (CCR-9417382, CISE/CCR ESS 9806747); Sun grant (EDUD-7826-990410-US); Faculty Fellowship of the Carroll School of Management, Boston College; U.K. Engineering and Physical Sciences Research Council (GR/L 36963, GR/L 15685

DataHub: Collaborative Data Science & Dataset Version Management at Scale

Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.Comment: 7 page

Interprocedural Type Specialization of JavaScript Programs Without Type Analysis

Dynamically typed programming languages such as Python and JavaScript defer type checking to run time. VM implementations can improve performance by eliminating redundant dynamic type checks. However, type inference analyses are often costly and involve tradeoffs between compilation time and resulting precision. This has lead to the creation of increasingly complex multi-tiered VM architectures. Lazy basic block versioning is a simple JIT compilation technique which effectively removes redundant type checks from critical code paths. This novel approach lazily generates type-specialized versions of basic blocks on-the-fly while propagating context-dependent type information. This approach does not require the use of costly program analyses, is not restricted by the precision limitations of traditional type analyses. This paper extends lazy basic block versioning to propagate type information interprocedurally, across function call boundaries. Our implementation in a JavaScript JIT compiler shows that across 26 benchmarks, interprocedural basic block versioning eliminates more type tag tests on average than what is achievable with static type analysis without resorting to code transformations. On average, 94.3% of type tag tests are eliminated, yielding speedups of up to 56%. We also show that our implementation is able to outperform Truffle/JS on several benchmarks, both in terms of execution time and compilation time.Comment: 10 pages, 10 figures, submitted to CGO 201

Using shared-data localization to reduce the cost of inspector-execution in unified-parallel-C programs

Programs written in the Unified Parallel C (UPC) language can access any location of the entire local and remote address space via read/write operations. However, UPC programs that contain fine-grained shared accesses can exhibit performance degradation. One solution is to use the inspector-executor technique to coalesce fine-grained shared accesses to larger remote access operations. A straightforward implementation of the inspector executor transformation results in excessive instrumentation that hinders performance.; This paper addresses this issue and introduces various techniques that aim at reducing the generated instrumentation code: a shared-data localization transformation based on Constant-Stride Linear Memory Descriptors (CSLMADs) [S. Aarseth, Gravitational N-Body Simulations: Tools and Algorithms, Cambridge Monographs on Mathematical Physics, Cambridge University Press, 2003.], the inlining of data locality checks and the usage of an index vector to aggregate the data. Finally, the paper introduces a lightweight loop code motion transformation to privatize shared scalars that were propagated through the loop body.; A performance evaluation, using up to 2048 cores of a POWER 775, explores the impact of each optimization and characterizes the overheads of UPC programs. It also shows that the presented optimizations increase performance of UPC programs up to 1.8 x their UPC hand-optimized counterpart for applications with regular accesses and up to 6.3 x for applications with irregular accesses.Peer ReviewedPostprint (author's final draft