26,098 papers · page 319 of 1,305
Quantization optimizes machine learning inference for resource constrained environments by reducing the precision of its computation. In the extreme, even single-bit computations can produce acceptable results at dramatically lower cost. But this ultra-low-precision quantization …
The popularity of data- and scientific-oriented applications, abundance of on-demand compute resources, and scarcity of domain expert programmers have given rise to high-level scripting languages. These high-level scripting languages offer a fast way to translate ideas into code,…
GPUs perform most efficiently when all threads in a warp execute the same sequence of instructions convergently. However, when threads in a warp encounter a divergent branch, the hardware serializes the execution of diverged paths. We consider a class of convergence opportunities…
This paper describes SIMD (Single Instruction Multiple Data) APIs for .NET that expose the parallel execution capabilities available on modern processors. These APIs include both platform-independent and platform-specific APIs that expose the SIMD capabilities available on the ta…
One of the key challenges arising when compilers vectorize loops for today’s SIMD-compatible architectures is to decide if vectorization or interleaving is beneficial. Then, the compiler has to determine the number of instructions to pack together and the interleaving level (stri…
In modern programming languages, automatic memory management has become a standard feature for allocating and freeing memory. In this paper, we show that the performance of today’s managed languages can degrade significantly due to cache contention among multiple concurrent appli…
We present a novel framework to automatically derive highly efficient parametric multi-way recursive divide&conquer algorithms for a class of dynamic programming (DP) problems. Standard two-way or any fixed R-way recursive divide&conquer algorithms may not fully exploit many-core…
Modern hardware is becoming increasingly susceptible to silent data corruptions. As general methods for detection and recovery from errors are time and energy consuming, selective detection and recovery are promising alternatives for applications that have the freedom to produce …
Graphics processing units (GPUs) have been commonly utilized to accelerate multiple emerging applications, such as big data processing and machine learning. While GPUs are proven to be effective, approximate computing, to trade off performance with accuracy, is one of the most co…
Many software applications are insecure because they misuse cryptographic APIs. Prior attempts to address misuses focused on detecting them after the fact. However, avoiding such misuses in the first place would significantly reduce development cost.
As interest in randomization has grown within the computing community, the number of pseudorandom value generators (PRVGs) at developers' disposal dramatically increased. Today, developers lack the tools necessary to obtain optimal behavior from their PRVGs. We provide the first …
This paper introduces a compiler optimization strategy for Software-Programmable Laboratories-on-a-Chip (SP-LoCs), which miniaturize and automate a wide variety of benchtop laboratory experiments. The compiler targets a specific class of SP-LoCs that manipulate discrete liquid dr…
Stencil computation is one of the most widely-used compute patterns in high performance computing applications. Spatial and temporal blocking have been proposed to overcome the memory-bound nature of this type of computation by moving memory pressure from external memory to on-ch…
Recent Spectre attacks exploit hardware speculative execution to read forbidden data. The attacks speculatively load forbidden data in misspeculated paths creating a side channel via the microarchitectural state which is not cleaned up after a misspeculation. The side channel the…
Increasing failures from transient faults necessitates the cost-efficient protection mechanism that will be always activated. Thus, we propose a novel prediction-based transient fault protection strategy as a low-cost software-only technique. Instead of re-executing expensive com…
Today, general-purpose memory allocators dominate the landscape of dynamic memory management. While these solutions can provide reasonably good behaviour across a wide range of workloads, it is an unfortunate reality that their behaviour for any particular workload can be highly …
We consider the problem of training machine learning models over multi-relational data. The mainstream approach is to first construct the training dataset using a feature extraction query over input database and then use a statistical software package of choice to train the model…
This paper presents the first general solution to the problem of optimizing both occupancy and Instruction-Level Parallelism (ILP) when compiling for a Graphics Processing Unit (GPU). Exploiting ILP (minimizing schedule length) requires using more registers, but using more regist…
Static analyses compute properties of programs that are true in all executions, and compilers use these properties to justify optimizations such as dead code elimination. Each static analysis in a compiler should be as precise as possible while remaining sound and being sufficien…
Programming environments for sequential programs provide strong debugging support. However, concurrent programs, especially under relaxed memory models, lack powerful interactive debugging tools. In this work, we present Gambit, an interactive debugging environment that uses gdb …