We present DCPerf, the first open-source performance benchmark suite actively used to inform procurement decisions for millions of CPU in hyperscale datacenters.Although numerous benchmarks exist, our evaluation reveals that they inaccurately project server performance for datacenter workloads or fail to scale to resemble production workloads on modern many-core servers.DCPerf distinguishes itself in two aspects: (1) it faithfully models essential software architectures and features of datacenter applications, such as microservice architecture and highly optimized multi-process or multi-thread concurrency; and (2) it strives to align its performance characteristics with those of production workloads, at both the system level and microarchitecture level.Both are made possible by our direct access to the source code and hyperscale production deployments of datacenter workloads.Additionally, we share real-world examples of using DCPerf in critical decision-making, such as selecting future CPU SKUs and guiding CPU vendors in optimizing their designs.Our evaluation demonstrates that DCPerf accurately projects the performance of representative production workloads within a 3.3% error margin across four generations of production servers introduced over a span of six years, with core counts varying widely from 36 to 176.

The US Department of Energy (DOE) has set a power target of 20-30MW on the first exascale machines. To achieve one exaflop under this power constraint, it is necessary to minimize wasteful consumption of power while striving to improve performance.

The US Department of Energy (DOE) has set a power target of 20-30MW on the first exascale machines. To achieve one exaFLOPS under this power constraint, it is necessary to manage power intelligently while maximizing performance. Most production-level parallel applications suffer from computational load imbalance across distributed processes due to non-uniform work decomposition. Other factors like manufacturing variation and thermal variation in the machine room may amplify this imbalance. As a result of this imbalance, some processes of a job reach the blocking calls, collectives or barriers earlier and wait for others to reach the same point. This waiting results in a wastage of energy and CPU cycles which degrades application efficiency and performance.

As we approach the exascale era, power has become a primary bottleneck. The US Department of Energy has set a power constraint of 20MW on each exascale machine. To be able achieve one exaflop under this constraint, it is necessary that we use power intelligently to maximize performance under a power constraint.