This work proposes a framework called FuSy that analyzes the data dependence graphs (DAGs) of two sparse kernels and creates an efficient schedule to execute the kernels in combination. Sparse kernels are frequently used in scientific codes and in ...

The existing Graph Neural Network (GNN) systems adopt graph partitioning to divide the graph data for multi-GPU training. Although they support large graphs, we find that the existing techniques lead to large data loading overhead. In this work, we for ...

Programming languages using functions on collections of values, such as map, reduce, scan and filter, have been used for over fifty years. Such collections have proven to be particularly useful in the context of parallelism because such functions are ...

Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in various combinatorial and graph algorithms as well as various bioinformatics and data analytics applications for computing inner-product similarities. For an important ...

We present GASS, an LLVM-based open-source compiler for NVIDIA GPU's SASS machine assembly. GASS is the first open-source compiler targeting SASS, and it provides a unified toolchain for currently fragmented low-level performance research on NVIDIA GPUs. ...

To facilitate programming with non-volatile memory (NVM), a set of memory persistency models, such as strict and epoch persistency, have been proposed. Although these models provide high-level guidance for reasoning about the data persistence, ...

Concurrent program verification is challenging due to a large number of thread interferences. A popular approach is to encode concurrent programs as SMT formulas and then rely on off-the-shelf SMT solvers to accomplish the verification. In most existing ...

Modern computing platforms tend to deploy multiple GPUs on a single node to boost performance. GPUs have large computing capacities and are an expensive resource. Increasing their utilization without causing performance degradation of individual ...

Recent desktop and mobile processors often integrate CPU and GPU onto the same die. The limited memory bandwidth of these integrated architectures can negatively affect the performance of data-parallel workloads when all computational resources are ...

The pursuit of power-efficiency is popularizing asymmetric multicore processors (AMP) such as ARM big.LITTLE, Apple M1 and recent Intel Alder Lake with big and little cores. However, we find that existing scalable locks fail to scale on AMP and cause ...

With the ever-shrinking size of transistors and increasing scale of applications, silent data corruptions (SDCs) have become a common yet serious issue in HPC applications. Selective instruction duplication (SID) is a popular fault-tolerance technique ...

Real-time data processing applications with low latency requirements have led to the increasing popularity of stream processing systems. While such systems offer convenient APIs that can be used to achieve data parallelism automatically, they offer ...

Performance variance is a serious problem for parallel applications, which can cause performance degradation and make applications' behavior hard to understand. Therefore, detecting and diagnosing performance variance are of crucial importance for users ...

Performance analysis is widely used to identify performance issues of parallel applications. However, complex communications and data dependence, as well as the interactions between different kinds of performance issues make high-efficiency performance ...

Rust is a modern systems language focused on performance and reliability. Complementing Rust's promise to provide "fearless concurrency", developers frequently exploit asynchronous message passing. Unfortunately, sending and receiving messages in an ...

We present KumQuat, a system for automatically generating data-parallel implementations of Unix shell commands and pipelines. The generated parallel versions split input streams, execute multiple instantiations of the original pipeline commands to ...