Microarchitectural design-space exploration of an in-order RISC-V processor in a 22nm CMOS technology

The purpose of this paper is to explore the trade-offs between IPC and maximum clock frequency in an in-order processor design. This work evaluates the impact on the performance and frequency of different pipeline optimizations. We target ASIC implementation using an advanced synthesis tool-flow with modern technology libraries. As a result, we can analyze the processor’s critical paths in a representative environment. In this paper, we analyze and modify Riscy, an in-order processor, taking into account the consequences of considering the ASIC target for this design. We have achieved a frequency of 1.3GHz and 2.03 CoreMark/MHz in the EEMBC CoreMark.Peer ReviewedPostprint (published version

AxleDB: A novel programmable query processing platform on FPGA

With the rise of Big Data, providing high-performance query processing capabilities through the acceleration of the database analytic has gained significant attention. Leveraging Field Programmable Gate Array (FPGA) technology, this approach can lead to clear benefits. In this work, we present the design and implementation of AxleDB: An FPGA-based platform that enables fast query processing for database systems by melding novel database-specific accelerators with commercial-off-the-shelf (COTS) storage using modern interfaces, in a novel, unified, and a programmable environment. AxleDB can perform a large subset of SQL queries through its set of instructions that can map compute-intensive database operations, such as filter, arithmetic, aggregate, group by, table join, or sort, on to the specialized high-throughput accelerators. To minimize the amount of SSD I/O operations required, AxleDB also supports hardware MinMax indexing for databases. We evaluated AxleDB with five decision support queries from the TPC-H benchmark suite and achieved a speedup from 1.8X to 34.2X and energy efficiency from 2.8X to 62.1X, in comparison to the state-of-the-art DBMS, i.e., PostgreSQL and MonetDB.The research leading to these results has received funding from the European Union Seventh Framework Program (FP7) (under the AXLE project GA number 318633), the Ministry of Economy and Competitiveness of Spain (under contract number TIN2015-65316-p), Turkish Ministry of Development TAM Project (number 2007K120610), and Bogazici University Scientific Projects (number 7060).Peer ReviewedPostprint (author's final draft

Accelerating Hash-Based Query Processing Operations on FPGAs by a Hash Table Caching Technique

Extracting valuable information from the rapidly growing field of Big Data faces serious performance constraints, especially in the software-based database management systems (DBMS). In a query processing system, hash-based computational primitives such as the hash join and the group-by are the most time-consuming operations, as they frequently need to access the hash table on the high-latency off-chip memories and also to traverse whole the table. Subsequently, the hash collision is an inherent issue related to the hash tables, which can adversely degrade the overall performance. In order to alleviate this problem, in this paper, we present a novel pure hardware-based hash engine, implemented on the FPGA. In order to mitigate the high memory access latencies and also to faster resolve the hash collisions, we follow a novel design point. It is based on caching the hash table entries in the fast on-chip Block-RAMs of FPGA. Faster accesses to the correspondent hash table entries from the cache can lead to an improved overall performance. We evaluate the proposed approach by running hash-based table join and group-by operations of 5 TPC-H benchmark queries. The results show 2.9×–4.4× speedups over the cache-less FPGA-based baseline.The research leading to these results has received funding from the European Union’s Seventh Framework Program (FP7/2007-2013), for Advanced Analytics for Extremely Large European Databases (AXLE) project under grant agreement number 318633, and from the Ministry of Economy and Competitiveness of Spain under contract number TIN2015-65316-p.Peer ReviewedPostprint (author's final draft

From plasma to beefarm: Design experience of an FPGA-based multicore prototype

In this paper, we take a MIPS-based open-source uniprocessor soft core, Plasma, and extend it to obtain the Beefarm infrastructure for FPGA-based multiprocessor emulation, a popular research topic of the last few years both in the FPGA and the computer architecture communities. We discuss various design tradeoffs and we demonstrate superior scalability through experimental results compared to traditional software instruction set simulators. Based on our experience of designing and building a complete FPGA-based multiprocessor emulation system that supports run-time and compiler infrastructure and on the actual executions of our experiments running Software Transactional Memory (STM) benchmarks, we comment on the pros, cons and future trends of using hardware-based emulation for research.Peer ReviewedPostprint (author's final draft

Functional verification of a RISC-V vector accelerator

We present the functional verification efforts for an academic RISC-V based vector accelerator, successfully taped-out in the context of the European Processor Initiative. For our novel RISC-V based decoupled vector accelerator, we built a verification infrastructure consisting of a UVM environment, performing step by step co-simulation of all vector instructions, using the Spike instruction set simulator as a reference model. Furthermore, for validating this complex design connected to a scalar core using a custom interface, we provided automated constrained-random test generation, simulation and error reporting, and CI/CD infrastructure. We found 3005 errors during this process and reached 95.79% functional coverage.This research has received funding from the European High Performance Computing Joint Undertaking (JU) under Framework Partnership Agreement No 800928 (European Processor Initiative) and Specific Grant Agreement No 101036168 (EPI SGA2). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and from Croatia, France, Germany, Greece, Italy, Netherlands, Portugal, Spain, Sweden, and Switzerland. The EPI-SGA2 project, PCI2022-132935 is also co-funded by MCIN/AEI /10.13039/501100011033 and by the UE NextGenerationEU/PRTR.Peer ReviewedPostprint (author's final draft

DVINO: A RISC-V vector processor implemented in 65nm technology

This paper describes the design, verification, implementation and fabrication of the Drac Vector IN-Order (DVINO) processor, a RISC-V vector processor capable of booting Linux jointly developed by BSC, CIC-IPN, IMB-CNM (CSIC), and UPC. The DVINO processor includes an internally developed two-lane vector processor unit as well as a Phase Locked Loop (PLL) and an Analog-to-Digital Converter (ADC). The paper summarizes the design from architectural as well as logic synthesis and physical design in CMOS 65nm technology.The DRAC project is co-financed by the European Union Regional Development Fund within the framework of the ERDF Operational Program of Catalonia 2014-2020 with a grant of 50% of total eligible cost. The authors are part of RedRISCV which promotes activities around open hardware. The Lagarto Project is supported by the Research and Graduate Secretary (SIP) of the Instituto Politecnico Nacional (IPN) from Mexico, and by the CONACyT scholarship for Center for Research in Computing (CIC-IPN).Peer ReviewedArticle signat per 43 autors/es: Guillem Cabo∗, Gerard Candón∗, Xavier Carril∗, Max Doblas∗, Marc Domínguez∗, Alberto González∗, Cesar Hernández†, Víctor Jiménez∗, Vatistas Kostalampros∗, Rubén Langarita∗, Neiel Leyva†, Guillem López-Paradís∗, Jonnatan Mendoza∗, Francesco Minervini∗, Julian Pavón∗, Cristobal Ramírez∗, Narcís Rodas∗, Enrico Reggiani∗, Mario Rodríguez∗, Carlos Rojas∗, Abraham Ruiz∗, Víctor Soria∗, Alejandro Suanes‡, Iván Vargas∗, Roger Figueras∗, Pau Fontova∗, Joan Marimon∗, Víctor Montabes∗, Adrián Cristal∗, Carles Hernández∗, Ricardo Martínez‡, Miquel Moretó∗§, Francesc Moll∗§, Oscar Palomar∗§, Marco A. Ramírez†, Antonio Rubio§, Jordi Sacristán‡, Francesc Serra-Graells‡, Nehir Sonmez∗, Lluís Terés‡, Osman Unsal∗, Mateo Valero∗§, Luís Villa† // ∗Barcelona Supercomputing Center (BSC), Barcelona, Spain. Email: [email protected]; †Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Mexico City, Mexico; ‡ Institut de Microelectronica de Barcelona, IMB-CNM (CSIC), Spain. Email: [email protected]; §Universitat Politecnica de Catalunya (UPC), Barcelona, Spain. Email: [email protected] (author's final draft

Profiling Transactional Memory applications on an atomic block basis: A Haskell case study

Abstract. In many recent works in Transactional Memory (TM), researchers have profiled TM applications based on execution data using lumped pertransaction averages. This approach both omits meaningful profiling information that can be extracted from the transactional program and hides potentially useful clues that can be used to discover the bottlenecks of the TM system. In this study, we propose partitioning transactional programs into executions of their atomic blocks (AB), observing both the individual properties of these ABs, and their effects on the overall execution on a Software Transactional Memory (STM) benchmark in Haskell. Profiling on the AB-level and focusing on the intra-AB relationships helps to (i) characterize transactional programs with per-AB statistics, (ii) examine the conflict relationships that are caused between the ABs, and thus (iii) to identify and classify the shared data that often cause conflicts. Through experimentation, we show that the AB behavior in most of the Haskell STM benchmark applications is quite heterogeneous, proving the need for such fine-grained, per-AB profiling framework.

AxleDB: A novel programmable query processing platform on FPGA

With the rise of Big Data, providing high-performance query processing capabilities through the acceleration of the database analytic has gained significant attention. Leveraging Field Programmable Gate Array (FPGA) technology, this approach can lead to clear benefits. In this work, we present the design and implementation of AxleDB: An FPGA-based platform that enables fast query processing for database systems by melding novel database-specific accelerators with commercial-off-the-shelf (COTS) storage using modern interfaces, in a novel, unified, and a programmable environment. AxleDB can perform a large subset of SQL queries through its set of instructions that can map compute-intensive database operations, such as filter, arithmetic, aggregate, group by, table join, or sort, on to the specialized high-throughput accelerators. To minimize the amount of SSD I/O operations required, AxleDB also supports hardware MinMax indexing for databases. We evaluated AxleDB with five decision support queries from the TPC-H benchmark suite and achieved a speedup from 1.8X to 34.2X and energy efficiency from 2.8X to 62.1X, in comparison to the state-of-the-art DBMS, i.e., PostgreSQL and MonetDB.The research leading to these results has received funding from the European Union Seventh Framework Program (FP7) (under the AXLE project GA number 318633), the Ministry of Economy and Competitiveness of Spain (under contract number TIN2015-65316-p), Turkish Ministry of Development TAM Project (number 2007K120610), and Bogazici University Scientific Projects (number 7060).Peer Reviewe

Accelerating Hash-Based Query Processing Operations on FPGAs by a Hash Table Caching Technique

Extracting valuable information from the rapidly growing field of Big Data faces serious performance constraints, especially in the software-based database management systems (DBMS). In a query processing system, hash-based computational primitives such as the hash join and the group-by are the most time-consuming operations, as they frequently need to access the hash table on the high-latency off-chip memories and also to traverse whole the table. Subsequently, the hash collision is an inherent issue related to the hash tables, which can adversely degrade the overall performance. In order to alleviate this problem, in this paper, we present a novel pure hardware-based hash engine, implemented on the FPGA. In order to mitigate the high memory access latencies and also to faster resolve the hash collisions, we follow a novel design point. It is based on caching the hash table entries in the fast on-chip Block-RAMs of FPGA. Faster accesses to the correspondent hash table entries from the cache can lead to an improved overall performance. We evaluate the proposed approach by running hash-based table join and group-by operations of 5 TPC-H benchmark queries. The results show 2.9×–4.4× speedups over the cache-less FPGA-based baseline.The research leading to these results has received funding from the European Union’s Seventh Framework Program (FP7/2007-2013), for Advanced Analytics for Extremely Large European Databases (AXLE) project under grant agreement number 318633, and from the Ministry of Economy and Competitiveness of Spain under contract number TIN2015-65316-p.Peer Reviewe

Dissecting transactional executions in Haskell

In this paper, we present a Haskell Transactional Memory benchmark in order to provide a comprehensive application suite for the use of Software Transactional Memory (STM) researchers. We develop a framework to profile the execution of the benchmark applications and to collect detailed runtime data on their transactional behavior. Using a composite of the collected raw data, we propose new transactional performance metrics. We analyze key statistics relative to critical regions, transactional log-keeping and overall transactional overhead and accordingly draw conclusions on the results of our extensive analysis of the set of applications. The results advance our comprehension on different characteristics of applications under the transactional management of the pure functional programming language, Haskell