We present a decade of evolution and production experience with Jupiter datacenter network fabrics. In this period Jupiter has delivered 5x higher speed and capacity, 30% reduction in capex, 41% reduction in power, incremental deployment and technology refresh all while serving live production traffic. A key enabler for these improvements is evolving Jupiter from a Clos to a direct-connect topology among the machine aggregation blocks. Critical architectural changes for this include: A datacenter interconnection layer employing Micro-Electro-Mechanical Systems (MEMS) based Optical Circuit Switches (OCSes) to enable dynamic topology reconfiguration, centralized Software-Defined Networking (SDN) control for traffic engineering, and automated network operations for incremental capacity delivery and topology engineering. We show that the combination of traffic and topology engineering on direct-connect fabrics achieves similar throughput as Clos fabrics for our production traffic patterns. We also optimize for path lengths: 60% of the traffic takes direct path from source to destination aggregation blocks, while the remaining transits one additional block, achieving an average block-level path length of 1.4 in our fleet today. OCS also achieves 3x faster fabric reconfiguration compared to pre-evolution Clos fabrics that used a patch panel based interconnect.

Abstract Graphical fluid simulations are CPU‐bound. Parallelizing simulations on hundreds of cores in the computing cloud would make them faster, but requires evenly balancing load across nodes. Good load balancing depends on manual decisions from experts, which are time‐consuming and error prone, or dynamic approaches that estimate and react to future load, which are non‐deterministic and hard to debug. This paper proposes Birdshot scheduling, an automatic and purely static load balancing algorithm whose performance is close to expert decisions and reactive algorithms without their difficulty or complexity. Birdshot scheduling's key insight is to leverage the high‐latency, high‐throughput, full bisection bandwidth of cloud computing nodes. Birdshot scheduling splits the simulation domain into many micro‐partitions and statically assigns them to nodes randomly. Analytical results show that randomly assigned micro‐partitions balance load with high probability. The high‐throughput network easily handles the increased data transfers from micro‐partitions, and full bisection bandwidth allows random placement with no performance penalty. Overlapping the communications and computations of different micro‐partitions masks latency. Experiments with particle‐level set, SPH, FLIP and explicit Eulerian methods show that Birdshot scheduling speeds up simulations by a factor of 2‐3, and can out‐perform reactive scheduling algorithms. Birdshot scheduling performs within 21% of state‐of‐the‐art dynamic methods that require running a second, parallel simulation. Unlike speculative algorithms, Birdshot scheduling is purely static: it requires no controller, runtime data collection, partition migration or support for these operations from the programmer.

Existing cloud computing control planes do not scale to more than a few hundred cores, while frameworks without control planes scale but take seconds to reschedule a job. We propose an asynchronous control plane for cloud computing systems, in which a central controller can dynamically reschedule jobs but worker nodes never block on communication with the controller. By decoupling control plane traffic from program control flow in this way, an asynchronous control plane can scale to run millions of computations per second while being able to reschedule computations within milliseconds.

Distributing a simulation across many machines can drastically speed up computations and increase detail. The computing cloud provides tremendous computing resources, but weak service guarantees force programs to manage significant system complexity: nodes, networks, and storage occasionally perform poorly or fail. We describe Nimbus, a system that automatically distributes grid-based and hybrid simulations across cloud computing nodes. The main simulation loop is sequential code and launches distributed computations across many cores. The simulation on each core runs as if it is stand-alone: Nimbus automatically stitches these simulations into a single, larger one. To do this efficiently, Nimbus introduces a four-layer data model that translates between the contiguous, geometric objects used by simulation libraries and the replicated, fine-grain objects managed by its underlying cloud computing runtime. Using PhysBAM particle-level set fluid simulations, we demonstrate that Nimbus can run higher detail simulations faster, distribute simulations on up to 512 cores, and run enormous simulations (1024 3 cells). Nimbus automatically manages these distributed simulations, balancing load across nodes and recovering from failures. Implementations of PhysBAM water and smoke simulations as well as an open source heat-diffusion simulation show that Nimbus is general and can support complex simulations. Nimbus can be downloaded from https://nimbus.stanford.edu.

In this paper, we introduce a new class of signature matrices for overloaded synchronous CDMA systems that have a very low complexity decoder. While overloaded systems are more efficient from the bandwidth point of view, the Maximum Likelihood (ML) implementation for decoding is impractical even for moderate dimensions. Simulation results show that the performance of the proposed decoder is very close to that of the ML decoder. Indeed, the proposed decoding scheme needs neither multiplication nor addition and requires only a few comparisons . Furthermore, the computational complexity and the probability of error vs. Signal to Noise Ratios (SNR) are derived analytically.