Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real applications that represent user requirements into benchmark suites, to guarantee high usability and widespread adoption of a new system. Given the significant investments in leadership-class supercomputers of the exascale era, this is even more important and necessitates alignment with a vision of Open Science and reproducibility. In this work, we present the JUPITER Benchmark Suite, which incorporates 16 applications from various domains. It was designed for and used in the procurement of JUPITER, the first European exascale supercomputer. We identify requirements and challenges and outline the project and software infrastructure setup. We provide descriptions and scalability studies of selected applications and a set of key takeaways. The JUPITER Benchmark Suite is released as open source software with this work at github.com/FZJ-JSC/jubench

Low latency and high throughput are critical features for 5G mobile communication systems and beyond, in which the support of large MIMO is essential. Signal detection in large Multiple-Input Multiple-Output (MIMO) is a paramount component of a communication system since its performance in terms of latency, error rate, and achieved throughput depends on it. In this paper, we demonstrate the ability of our proposed massively parallel non-linear detection approach to support a large number of antennas and sustain high throughput at the extreme low latency of next-generation mobile communication systems. Our proposed method operates on a search tree that models all possible combinations of the transmitted signal. It selects coefficients from different levels and navigates the tree toward the Maximum Likelihood (ML) solution. To maintain the low latency requirement, we leverage the significant computational power of the Graphics Processing Unit (GPU) by expressing operations in terms of matrix-matrix multiplications. The obtained results show the ability of our non-linear detection approach to deal with up to 120 antennas with one-millisecond latency while satisfying good error rate performance at a practical signal-to-noise ratio (SNR).

Wireless communication systems rely on aggressive spatial multiplexing Multiple-Input Multiple-Output (MIMO) access points to enhance network throughput. A significant computational hurdle for large MIMO systems is signal detection and decoding, which has exponentially increasing computational complexity as the number of antennas increases. Hence, the feasibility of large MIMO systems depends on suitable implementations of signal decoding schemes.This paper presents an FPGA-based Sphere Decoder (SD) architecture that provides high-performance signal decoding for large MIMO systems, supporting up to 16-QAM modulation. The SD algorithm is refactored to map well to the FPGA architecture using a GEMM-based approach to exploit the parallel computational power of FPGAs. We implement FPGA-specific optimization techniques to improve computational complexity. We show significant improvement in time to decode the received signal with under 10–2BER. The design is deployed on a Xilinx Alveo U280 FPGA and shows up to a 9× speedup compared to optimized multi-core CPU execution, achieving real-time requirements. Our proposed design reduces power consumption by a geo-mean of 38.1× compared to CPU implementation, which is important in real-world deployments. We also evaluate our design against alternative approaches on GPU.

Branch and Bound (B&B) algorithms represent a well-known tool for optimally solving combinatorial optimization problems in general and scheduling problems in particular. They have been widely used as a reference for classical scheduling problems such-as job shop scheduling. However, they rely on a deep knowledge of the problem to achieve fast convergence and low execution time. To deal efficiently with real-world problems where we do not have prior knowledge, we investigate the use of learning-based methods to accelerate the B&B tree exploration in this paper. We use the Blocking Job Shop Scheduling Problem (BJSSP) as a study case. Our approach aims to learn an efficient branching and selection process by training a model using small and medium BJSSP benchmarks. For each benchmark, two phases are used. First, we train the model using a small set of solutions provided by a metaheuristic to detect similar features among good solutions. Therefore, generating branching and selection roles. After that, we apply these roles to solve these instances optimally. The obtained results show the effectiveness of our proposed learning-based selection by achieving performance results near the best B&B implementation for BJSSP known to us.

Graph Edit Distance (GED) is a well-known measure used in the graph matching to measure the similarity/dissimilarity between two graphs by computing the minimum cost of edit operations needed to transform one graph into another. This process, Which appears to be simple, is known NP-hard and time consuming since the search space is increasing exponentially. One way to optimally solve this problem is by using Branch and Bound (B&B) algorithms, Which reduce the computation time required to explore the whole search space by performing an implicit enumeration of the search space instead of an exhaustive one based on a pruning technique. nevertheless, They remain inefficient when dealing with large problem instances due to the impractical running time needed to explore the whole search space. To overcome this issue, We propose in this paper three parallel B&B approaches based on shared memory to exploit the multi-core CPU processors: First, a work-stealing approach where several instances of the B&B algorithm explore a single search tree concurrently achieving speedups up to 24 × faster than the sequential version. Second, a tree-based approach where multiple parts of the search tree are explored simultaneously by independent B&B instances achieving speedups up to 28 × . Finally, Due to the irregular nature of the GED problem, two load-balancing strategies are proposed to ensure a fair workload between parallel processes achieving impressive speedups up to 300 × . all experiments have been carried out on well-known datasets