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Non-Uniform Memory Access (NUMA) architectures are widely used in mainstream multi-socket computer systems to scale memory bandwidth. Without a NUMA-aware design, programs can suffer from significant performance degradation due to inter-socket bandwidth contention. However, identifying bandwidth contention is challenging. Existing methods measure bandwidth consumption. However, consumption alone is...
The Intel® Xeon Phi™ is gaining popularity for high-performance computing (HPC) applications, but the performance of this many-core coprocessor with wide floating point SIMD units has yet to be explored on data analytics workloads. We construct a benchmark suite to explore the Xeon Phi™'s potential for use in data center servers. Our resulting PhiBench consists of eight representative data analytics...
Memory access latency continues to be a dominant bottleneck in a large class of applications on modern architectures. To optimize memory performance, it is important to utilize the locality in the memory hierarchy. Structure splitting can significantly improve memory locality. However, pinpointing inefficient code and providing insightful guidance for structure splitting is challenging. Existing tools...
Redundant computations can severely degrade performance in HPC applications. Redundant computations arise due to various causes such as developers' inattention to performance, inappropriate choice of algorithms, and inefficient code generation, among others. Aliasing, limited optimization scopes, and insensitivity to input and execution contexts act as severe deterrents to static program analysis...
It is difficult to scale parallel programs in a system that employs a large number of cores. To identify scalability bottlenecks, existing tools principally pinpoint poor thread synchronization strategies or unnecessary data communication. Memory subsystem is one of the key contributors to poor parallel scaling in multicore machines. State-of-the-art tools, however, either lack sophisticated capabilities...
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