736 papers · page 14 of 37
Tensor contractions are higher dimensional generalizations of matrix-matrix multiplication. They form the compute-intensive core of many applications in computational science and data science. In this paper, we describe a high-performance GPU code generator for arbitrary tensor c…
This paper shows how to extend sparse tensor algebra compilers to introduce temporary tensors called workspaces to avoid inefficient sparse data structures accesses. We develop an intermediate representation (IR) for tensor operations called concrete index notation that specifies…
Popular language extensions for parallel programming such as OpenMP or CUDA require considerable compiler support and runtime libraries and are therefore only available for a few programming languages and/or targets. We present an approach to vectorizing kernels written in an exi…
Many programs or algorithms are largely parameterized, especially those based on heuristics. The quality of the results depends on the parameter setting. Different inputs often have different optimal settings. Program tuning is hence of great importance. Existing tuning technique…
Nowadays, many applications use GPUs (Graphics Processing Units) to achieve high performance. When we use GPU servers, the idle CPU resource of the servers is often ignored. In this paper, we explore the idea: using the idle CPU resource to speed up GPU programs. We design a dyna…
This paper presents an approach and a tool, CASM-VERIFY, to automatically check the equivalence of highly optimized assembly implementations of cryptographic algorithms. The key idea of this paper is to decompose the equivalence checking problem into several small sub-problems us…
Buffer overflow is possibly the most well-known memory issue. It can cause erratic program behavior, such as incorrect outputs and crashes, and can be exploited to issue security attacks. Detecting buffer overflows has drawn significant research attention for almost three decades…
The massively parallel architecture of GPU accelerators are being harnessed to expedite computational workloads in cutting edge scientific research. Unfortunately writing applications for GPUs requires extensive knowledge of the underlying architecture, the application and the in…
Simplified parallel programming coupled with an ability to express speculative computation is realized with Software Transactional Memory (STM). Although STMs are gaining popularity because of significant improvements in parallel performance, they exhibit enormous variation in tr…
Most high performance computing clusters are nowadays composed of large multicore machines that expose Non-Uniform Memory Access (NUMA), and they are interconnected using modern communication paradigms, such as Remote Direct Memory Access (RDMA). In this work we perform a study o…
Productivity languages such as Python and R are growing in popularity especially in the development of data analysis algorithms. While these languages provide powerful tools to accelerate the development process, they incur heavy overhead due to their interpreted nature. One appr…
Performance optimization for large-scale applications has recently become more important as computation continues to move towards data centers. Data-center applications are generally very large and complex, which makes code layout an important optimization to improve their perfor…
SLP Auto-vectorization converts straight-line code into vector code. It scans input code for groups of instructions that can be combined into vectors and replaces them with their corresponding vector instructions. This work introduces Super-Node SLP (SN-SLP), a new SLP-style algo…
Inlining is one of the most important compiler optimizations. It reduces call overheads and widens the scope of other optimizations. But, inlining is somewhat of a black art of an optimizing compiler, and was characterized as a computationally intractable problem. Intricate heuri…
Optimizing data-intensive applications such as image processing for GPU targets with complex memory hierarchies requires to explore the tradeoffs among locality, parallelism, and computation. Loop fusion as one of the classical optimization techniques has been proven effective to…
Resource-constrained devices for embedded systems are becoming increasingly important. In such systems, memory is highly restrictive, making code size in most cases even more important than performance. Compared to more traditional platforms, memory is a larger part of the cost a…
This paper presents a technique to generate in-bounds inputs for arrays used in memory-unsafe programming languages, such as C and C++. We show that most memory indexation found in actual C programs follows patterns that are easy to analyze statically. Based on this observation, …
With the popularity of Node.js, asynchronous, event-driven programming has become widespread in server-side applications. While conceptually simple, event-based programming can be tedious and error-prone. The complex semantics of the Node.js event loop, coupled with the different…
We discuss the design and the implementation of Locus, a system and a language to orchestrate the optimization of applications. The increasing complexity of machines and the large space of program variants, produced by the many transformations available, conspire to make compiler…
The throughput of B+ tree query processing is critical to many databases, file systems, and cloud applications. Based on bulk synchronous parallel (BSP), latch-free B+ tree query processing has shown promise by processing queries in small batches and avoiding the use of locks. As…